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Evidence for Divergent Evolution of Growth Temperature Preference in Sympatric Saccharomyces Species

  • Paula Gonçalves ,

    Affiliation Departamento de Ciências da Vida, Centro de Recursos Microbiológicos (CREM), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

  • Elisabete Valério,

    Affiliation Departamento de Ciências da Vida, Centro de Recursos Microbiológicos (CREM), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

  • Cláudia Correia,

    Affiliation Departamento de Ciências da Vida, Centro de Recursos Microbiológicos (CREM), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

  • João M. G. C. F. de Almeida,

    Affiliation Departamento de Ciências da Vida, Centro de Recursos Microbiológicos (CREM), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

  • José Paulo Sampaio

    Affiliation Departamento de Ciências da Vida, Centro de Recursos Microbiológicos (CREM), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal


The genus Saccharomyces currently includes eight species in addition to the model yeast Saccharomyces cerevisiae, most of which can be consistently isolated from tree bark and soil. We recently found sympatric pairs of Saccharomyces species, composed of one cryotolerant and one thermotolerant species in oak bark samples of various geographic origins. In order to contribute to explain the occurrence in sympatry of Saccharomyces species, we screened Saccharomyces genomic data for protein divergence that might be correlated to distinct growth temperature preferences of the species, using the dN/dS ratio as a measure of protein evolution rates and pair-wise species comparisons. In addition to proteins previously implicated in growth at suboptimal temperatures, we found that glycolytic enzymes were among the proteins exhibiting higher than expected divergence when one cryotolerant and one thermotolerant species are compared. By measuring glycolytic fluxes and glycolytic enzymatic activities in different species and at different temperatures, we subsequently show that the unusual divergence of glycolytic genes may be related to divergent evolution of the glycolytic pathway aligning its performance to the growth temperature profiles of the different species. In general, our results support the view that growth temperature preference is a trait that may have undergone divergent selection in the course of ecological speciation in Saccharomyces.


The genus Saccharomyces is best known for its most emblematic member, the model and industrial yeast S. cerevisiae, but comprises in addition eight closely related species [1][3]. Since the beginning of pure culture methods, the repeated isolation of S. cerevisiae from wine, beer and other fermented beverages, and the difficulty in finding its truly natural habitats, has lead to the common view that this species was a product of domestication [4][6]. Hence, S. cerevisiae was thought to have originated at the onset of human driven fermentations, which have been traced back to 5,000–10,000 years ago [7], [8], and to have been essentially confined to the vicinity of artificial fermentation environments ever since. As to the other species of the genus for which significant records are available, S. bayanus has been connected to beer [9], S. uvarum is often associated with cider and wine fermented at low temperatures [10], [11] and S. pastorianus (synonym S. carlsbergensis) is used for the production of lager beer [12]. These three species were also regarded as domesticated and since they ferment well at low temperatures, they are often referred to as cryotolerant [13].

Recent studies have challenged the domestication model by providing evidence for a natural habitat of S. cerevisiae, the bark of certain oak trees in North America and the soil surrounding them, where persistent populations can be found [14]. Subsequently, other studies have shown that this species is composed of both domesticated and wild populations and that domesticated populations derive from their wild relatives [15]. This progress marked the beginning of a new age for this model organism and its siblings, as basic aspects of their ecology can in principle be tested, paving the way for the vast wealth of knowledge concerning their biology to be examined within an ecological framework [16]. The population structure of S. cerevisiae [17][19] and of its sibling S. paradoxus [20][22], a species that is not associated with man-driven fermentations, were extensively studied. Those studies showed that whereas the genetic structure of S. paradoxus exhibited a strong influence of geography, a different pattern dominated mostly by ecological factors emerged for S. cerevisiae [23], [24]. More recently, the habitats and biogeography of the various species of the genus Saccharomyces were investigated with a modified isolation protocol that employed a parallel enrichment step at two temperatures (high and low) to maximize the chances of finding both thermotolerant (e.g. S. cerevisiae and S. paradoxus) and cryotolerant species [25]. This approach provided evidence that several Saccharomyces species co-existed in the same habitat, in some cases even in the same tree in temperate ecosystems in North America, Central Europe and the Mediterranean. Sympatry seemed to be related with preferences of the species for different growth temperatures. For example, S. cerevisiae (thermotolerant) was frequently found in association with S. kudriavzevii, a cryotolerant species with markedly lower maximum growth temperatures. S. paradoxus (thermotolerant) was similarly found in sympatry with S. uvarum (cryotolerant) [25]. The coexistence of S. cerevisiae and S. kudriavzevii was confirmed in other studies that investigated also the Mediterranean ecosystem [26] and in North America a case of sympatry between S. cerevisiae and S. paradoxus [14] was explained by different thermal growth profiles [27].

Since differences in growth temperature preferences are by far the strongest phenotypic discontinuity between sympatric Saccharomyces species, our working hypothesis is that the distinct temperature regimes allow the sympatric partners to explore different (temperature) niches within the same habitat, thereby avoiding competitive exclusion. This impelled us to consider the possibility that this trait may have undergone ecologically based divergent selection in some of the species, in line with a hypothetical ecological speciation scenario [28].

All the species in the genus Saccharomyces share physiological characteristics unusual among fungi that consist in a marked preference to ferment sugars when they are present at sufficiently high concentrations, irrespectively of oxygen availability [29], [30]. This typical Saccharomyces fermentative life strategy is thought to have evolved after a whole genome duplication (WGD) that occurred approximately 100 MYA ago [31], [32] and was followed by a period of dramatic genome remodeling with massive gene loss erasing unwarranted redundancy [33]. The evolution of post-WGD genome architecture has been extensively studied [32], [34], [35], yielding important insights on several aspects of the fate of genes that remained duplicated. Subfunctionalization [32][34], [36][38] as well as gene dosage effects [39] seem to have contributed for the retention of genes in duplicate. A significant number of glycolytic genes fall under the latter category, since five of the ten glycolytic reactions maintain WGD duplicates in S. cerevisiae without evidence for marked subfunctionalization. An attractive hypothesis was put forward stating that an increase in glycolytic flux following WGD may have provided the basis for the subsequent evolution of the typical Saccharomyces strategy, which consists in the rapid conversion of sugars into ethanol, to secure the carbon-source and take advantage of ethanol toxicity for competitors [34], [39], [40].

In the present work we used a comparative genomics approach and publicly available complete genome sequences of five Saccharomyces species [41], [42] to search for proteins exhibiting molecular evolution patterns suggestive of divergent selection, when species with different growth temperature preferences are compared. The dN/dS ratio [rate of nonsynonymous substitutions per nonsynonymous site (dN)/ rate of synonymous substitutions per synonymous site (dS)] of the complete Saccharomyces ORFeomes was employed as a measure of protein divergence corrected for phylogenetic distance. Saccharomyces genomic sequence data has been previously used to determine dN/dS ratios both to detect genes containing the signature of adaptive selection [43][45] and to evaluate the extent of co-evolution of functionally related genes [46]. Unlike those investigations, our study was directed towards uncovering protein divergence specifically related to differences in growth temperature preference. We addressed this question by determining the dN/dS ratios of the complete ORFeomes of four Saccharomyces species in pair-wise comparisons with S. cerevisiae and focused our attention in significant changes in the dN/dS ratios determined between different pairs of ortologous genes. As a result, we identified genes that are uncommonly divergent between S. cerevisiae (thermotolerant) and S. uvarum (cryotolerant). Among the genes found, some had been previously associated with adaptation to growth at suboptimal temperatures. In addition, one unforeseen functional category encompassing the genes encoding glycolytic enzymes also appeared to be significantly over-represented among those exhibiting higher dN/dS ratios in the comparison between S. cerevisiae and S. uvarum. This prompted us to examine catabolic fluxes at different temperatures in Saccharomyces species with different growth temperature preferences. Our results brought to light species-specific trends, in line with an adaptation of glycolysis to different temperature ranges during Saccharomyces evolution and generally support an ecological speciation model.

Results and Discussion

Temperature determines the outcome of the competition between sympatric Saccharomyces species

Figure 1 depicts schematically the phylogenetic relationships of the species in the genus Saccharomyces and assigns them to the thermotolerant or cryotolerant groups. S. cerevisiae and S. kudriavzevii, belonging respectively to the former and latter categories, were found in our previous survey to exist in sympatry at several locations in Portugal [25]. In these cases, S. cerevisiae was the species isolated when an incubation temperature of 30°C was employed during the enrichment step, while S. kudriavzevii was isolated if the same sample was incubated at 10°C. In the same study a similar situation was found for S. paradoxus and S. uvarum which were sympatric in North America. In order to test the hypothesis that temperature plays a key role in the coexistence of the two populations by contributing for niche divergence and therefore opposing competitive exclusion, we set up a series of competition experiments between a pair of sympatric S. cerevisiae (ZP 567) and S. kudriavzevii (ZP 591) strains isolated from the same sample of oak bark in Portugal (Table S1A). Both strains were pre-grown separately at the temperature set for each experiment, and were subsequently inoculated in approximately equal amounts in fresh growth medium and co-cultured at a given temperature until stationary phase. These experiments were performed at different temperatures between 5°C and 30°C. The relative size of the two populations was quantified in the beginning and at the end of each co-culture by qReal-Time PCR, using primers specific for each species (Table S1B). The results are shown in Figure 2. Clearly, at the most extreme temperatures one of the species dominates the co-culture in line with its growth temperature preference, while at around 20°C, the two populations remain equilibrated at the end of the co-culture. Similar experiments involving sympatric S. uvarum and S. paradoxus strains yielded comparable results (Figure S1). Since in the locality of the isolation of S. cerevisiae ZP 567 and S. kudriavzevii ZP 591 the average low temperatures from November to April range between 5°C and 9°C and from June to September average high temperatures range between 27°C and 32°C, the long-term co-existence of the two populations could be explained by reciprocal fitness variation along a temperature gradient. Hence, these experiments support the hypothesis that temperature is an important ecological determinant modulating the co-existence of two sympatric populations, circadian and / or annual temperature oscillations favoring each species in turn. We searched also for negative effects of one population over the other, like production of killer toxins [16], [48]. Such type of competition by interference was not observed for the two pairs of strains examined (results not shown).

Figure 1. Schematic representation of the phylogeny of the genus Saccharomyces.

The tree topology combines information from genome-wide studies [47], rDNA sequence data [3], four concatenated nuclear genes [23] and our own sequence analyses. The maximum growth temperature of each species is indicated on the right side (data from this study).

Figure 2. Outcome of competition between sympatric Saccharomyces species.

Relative fitness of S. cerevisiae ZP 567 competing with S. kudriavzevii ZP 591 at different temperatures is shown. The relative size of the populations of both species was determined in the beginning and at the end of each co-culture experiment by qRT-PCR using species specific primers, and was used to calculate the relative fitness of each species.

Protein evolution rates in Saccharomyces: cryotolerant vs thermotolerant species

In the course of the adaptation of two putative ancestral Saccharomyces sub-populations to distinct niches, as would happen in an ecological speciation scenario, genes important for niche differentiation may have undergone divergent selection [28]. A previous study identified a number of genes exhibiting protein evolutionary rate changes as well as two functional categories that are particularly divergent between S. cerevisiae and S. paradoxus, but no lineage specific functional biases were uncovered for the other lineages investigated [44]. This study did not focus on any particular phenotype, and its conclusions do not seem to bear a particular relation to the divergence of growth temperature preferences between Saccharomyces species. Since this trait is presently the most prominent phenotypic difference between sympatric Saccharomyces species, it is also, in our view, the most likely to have experienced divergent selection. To examine this possibility, we looked for genes that exhibit significantly higher dN/dS ratios when measured between S. cerevisiae and S. uvarum, then when measured between S. cerevisiae and S. paradoxus. The reasoning behind this is that the first pair of species comprises one thermotolerant and one cryotolerant species, while the second involves two species with similar growth temperature preferences and can therefore be used as a reference. A database of orthologous Saccharomyces ORFs was built using the BiDiBlast pipeline [49], which also calculated the dN/dS ratios for all the orthologous gene pairs that could be reliably identified (Table S2). Subsequently, a comparison of the dN/dS ratios determined for S. cerevisiae and S. paradoxus, with the dN/dS ratios of the same orthologous gene pair determined between S. cerevisiae and S. uvarum was performed. Identification of genes exhibiting higher dN/dS ratios measured between S. cerevisiae and S. uvarum was facilitated by plotting the respective values obtained for the complete ORFeome in the two species pairwise comparisons. As shown in Figure 3, the data points corresponding to most of the orthologous gene pairs are concentrated along a diagonal line with a positive slope, a consequence of the fact that most orthologs have similar dN/dS ratios in both species pairwise comparisons. The slope of the regression line is lower than one, indicating that the dN/dS ratios of most ORFs are higher in the S. cerevisiae/S. paradoxus comparison (Figure 3, Figure S2). This could be an indirect consequence of the greater phylogenetic distance between S. cerevisiae and S. uvarum, because some genes are likely to exhibit saturated and thus underestimated variation at synonymous sites (dS values) in this pairwise comparison. We subsequently focused on ortholog pairs that fall clearly outside the average distribution, as a result of exhibiting markedly different dN/dS ratios in the two pairwise species comparisons. The genes exhibiting significantly higher dN/dS ratios in the S. cerevisiae/S.uvarum comparison correspond to data points located above the interval defined by the dashed lines in Figure 3. It should be noted that this sorting of “accelerated” vs “non- accelerated genes” (explained in more detail in the methods section) is quite stringent so that all outliers have experienced a substantial change in dN/dS ratio between the two species pairwise comparisons.

Figure 3. Comparison of dN/dS ratios of Saccharomyces orthologous gene pairs in different species comparisons.

dN/dS ratios depicted were determined between S. uvarum and S. cerevisiae (Yaxis) and between S. paradoxus and S. cerevisiae (Xaxis). Only orthologous gene pairs with dN/dS ratios below 0.4 in both species comparisons are shown (see Figure S2 for a plot containing the complete data set). Each point corresponds to one orthologous gene pair that could be reliably identified in both species comparisons. The regression line is shown in solid blue.The dashed lines add the contribution of the residuals distribution. Points outside these lines may be considered as significantly different from the general trend. The group of orthologs of interest to the present analysis displays higher dN/dS ratios in the comparison between the S. uvarum and S. cerevisiae genomes than in the comparison between S. paradoxus and S. cerevisiae, corresponding therefore to data points located above the segment defined by the dashed lines. Data points representing glycolytic genes are highlighted as red circles.

The outliers were subsequently manually identified, purged of residual artifacts caused by incorrect alignments and functionally or structurally related genes were grouped. Because all the species in the analysis are phenotypically similar and phylogenetically relatively close, large differences between the expression levels of orthologous genes are not expected. Hence, codon bias related constraints on the evolution of synonymous sites (dS), which are related to the level of expression [44] are expected to be similar among members of orthologous sets and are therefore unlikely to cause significant artefactual variation in dN/dS ratios in our analysis. Concurrent with this, it has been previously shown that variations in dN/dS ratios among Saccharomyces lineages for genes with dS values <1 is mostly due to variation at nonsynonymous sites (dN) [44].

Among the outlier genes, some encode proteins previously shown to be important for growth at suboptimal temperatures in Saccharomyces cerevisiae, namely TIF1 encoding a transcription initiation factor with helicase activity [50], NSR1, which is a protein required for rRNA processing [51] and several cell wall mannoproteins (TIR1, TIR2 and TIR4; [52]).

We subsequently explored the possibility that some functional/structural categories might be overrepresented in the accelerated group of genes. To this end categories having multiple genes in the accelerated group were manually examined in more detail. Three categories of genes were found to be significantly accelerated (Table 1). Notably, in these categories, very few”accelerated” genes exhibited dS values>1 that could be indicative of saturation (Table S2), therefore, increased dN/dS ratios in the S. uvarum/S. cerevisiae comparison are unlikely to be due to underestimation of dS values in these cases. The first category of “accelerated” genes consisted of cell wall mannoproteins with seven genes displaying accelerated evolution in the S. cerevisiae/S. uvarum comparison out of nine genes present in our analysis (Table 1). The second category encompasses the ribosomal components in charge of recruiting translation elongation factors (ribosomal stalk proteins) and subunits of translation elongation factors, suggesting that this process may have evolved as a whole more rapidly during divergence between thermotolerant and cryotolerant species. Finally, at least seven glycolytic genes are also significantly accelerated, which indicates that central carbon metabolism may have diverged more than average in the S. cerevisiae/S. uvarum comparison. Changes in dN/dS ratios of glycolytic genes in other species comparisons involving S. kudriavzevii and S. mikatae were also examined (Table S3). S. cerevisiae/S. kudriavzevii represents a second comparison involving a thermotolerant and a cryotolerant species. In this comparison, several glycolytic genes also exhibit considerably higher dN/dS ratios than in the S. cerevisiae/S.paradoxus comparison. On the other hand, in the comparison between S. cerevisiae and S. mikatae, which has intermediate growth temperature preferences (Figure 1) dN/dS ratios of glycolytic genes are generally lower (Table S3).

Table 1. Functional categories with elevated dN/dS in the S. cerevisiae/S. uvarum comparison

These results led us to put forward the hypothesis that the glycolytic pathway may have been under selective pressure to adapt to the different growth temperature regimes during evolution of Saccharomyces species. This would be in line with results previously obtained by others, showing that in S. cerevisiae, the enzymatic activities of most glycolytic enzymes show very pronounced temperature dependence [53]. As noted for S. cerevisiae, losses in glycolytic enzymatic activities are difficult to compensate for with increased expression, because no individual enzyme seems to exert a substantial amount of control over the total flux [53]. This means that overexpression of a large subset or even all the enzymes would be required to increase the flux. Glycolytic genes are already expressed at very high levels so that a significant further increase in expression would be likely to have seriously detrimental effects on fitness. Therefore, it is in our view a plausible hypothesis that the performance of the glycolytic pathway was adjusted by natural selection to the growth temperature preferences of different species and that this might have involved both evolution of the coding regions of glycolytic genes in the different species and concomitant small adjustments at the level of transcriptional regulation. However, it should be noted that at least one glycolytic enzyme in S. cerevisiae (Hxk2p) has an important regulatory role in the main glucose repression pathway in addition to its role in glycolysis [54]. Moreover, some glycolytic enzymes have been shown to be located in the cell wall as well as in the cytoplasm of several yeast species [55], [56] and it is so far unclear what their function might be at this location. Therefore, it is pertinent to question whether increased dN/dS ratios of glycolytic genes are actually linked with operation of the glycolytic pathway or if, alternatively, other established or putative secondary functions of glycolytic enzymes may pertain to the pattern of evolution of these genes. We subsequently proceeded to evaluate the plausibility of these alternate possibilities by measuring the total glycolytic flux at different temperatures in Saccharomyces species with different growth temperature preferences.

Species-specific effects of temperature on glycolytic flux

Changes in the performances of glycolytic enzymes at different temperatures are only expected to have an effect on organismal fitness if they have an impact on the overall glycolytic flux. Therefore, we measured the glycolytic flux at different temperatures in several strains of S. cerevisiae (thermotolerant), S. kudriavzevii and S. uvarum (both cryotolerant). Between two and six strains were tested per species in order to be able to distinguish species-specific trends from strain-specific effects which are much less likely to be related with the pattern of protein evolution. The glycolytic flux and respiratory activity were measured with a Warburg volumetric apparatus, which allows the quantification of produced CO2 and consumed O2 during metabolization of glucose by resting cells, at a set temperature [57]. As expected, we found some intra-specific variation in the glycolytic fluxes measured (Figure 4; Table S4). However, it was also possible to unravel species-specific trends. At 10°C, S. kudriavzevii exhibited significantly (p<0.0001) higher glycolytic flux than S. cerevisiae (Figure 4). Notably, the higher glycolytic flux in S. kudriavzevii is accompanied by the lowest respiratory activity of all three species (p<0.0001), which means that the increased flux is strongly directed to ethanol production. On the contrary, the other cryotolerant species, S. uvarum, seems to have a higher respiratory capacity than S. cerevisiae at 10°C (p = 0.02), and clearly higher than S. kudriavzevii (p<0.0001), but an intermediate glycolytic flux.

Figure 4. Glycolytic and respiratory fluxes at different temperatures.

Fluxes were measured at10°, 30° and 35°C in S. kudriavzevii, S. uvarum and S. cerevisiae. Each box and whisker plot depicts the results of experiments concerning one species and one temperature but involving multiple strains (see Table S4 for primary data). Boxes are delimited by the upper (75th) and lower (25th) percentiles of the experimental values and the horizontal line inside the box denotes the median of the complete data set. Minimum and maximum values are shown as asterisks and the mean value as a small open square.

The glycolytic flux at higher temperatures was subsequently measured at 30°C and at 35°C (Figure 4). In S. cerevisiae, the flux at 35°C is higher than at 30°C (p = 0.0006), implying that all the enzymes of the glycolytic pathway are sufficiently heat resistant to cope with this temperature (Figure 4). On the contrary, at 35°C the glycolytic flux does not increase in S. uvarum. For S. kudriavzevii, there may be even a small decrease of the glycolytic flux at 35°C when compared to 30°C (p = 0.09) (Figure 4). Notably, S. uvarum and S. kudriavzevii exhibit similar glycolytic rates but very different growth rates at these higher temperatures: while S. kudriavzevii grows very poorly already at 30°C [25], and fails to grow at 35°C, one of the S. uvarum strains tested (CBS 7001) exhibited similar growth rates at 30° and 25°C (results not shown). Taken together, these results suggest that the failure of S. kudriavzevii to grow well at temperatures above 30°C does not seem to be related with a limitation at the level of the glycolysis but that another process essential for growth is compromised at higher temperatures. Hence, our results suggest that evolution of the glycolytic pathway lead in S. kudriavzevii to a better performance at lower temperatures, possibly concomitant with a loss of performance at higher temperatures, which nevertheless does not seem to be the growth limiting factor for this species at 30°C.

Contrary to what was observed at 10°C, there was no clear difference between respiration rates in S. uvarum and S. cerevisiae at 30° and 35°C (Figure 4).

Temperature adaptation of the first steps of glycolysis

We next considered the activity of individual steps of glycolysis. To ascertain if species-specific trends could be uncovered for independent glycolytic reactions, enzymatic activities were measured in cell-free extracts of a similar set of S. cerevisiae, S. kudriavzevii and S. uvarum strains used in the experiments described in the previous section.

The glucose phosphorilation step in glycolysis potentially involves three enzymes (Hxk2p, Hxk1p and Glk1p) which are expressed under different growth conditions in S. cerevisiae [58]. In the presence of abundant fermentable sugars, only Hxk2p is expressed, while the genes encoding the other two enzymes are repressed. HXK2 is, in turn, repressed at low sugar concentrations or in the absence of sugar [59]. To evaluate if the pattern of expression was the same in the three Saccharomyces species under study, we determined the expression of HXK1 and HXK2 by quantitative Real-Time Reverse Transcription PCR in two strains of each species cultivated in the presence of high glucose concentrations. Surprisingly, the results show that HXK1 is the most expressed gene in the two S. uvarum strains, in sharp contrast to the S. cerevisiae strains which, as previously observed [60], expressed HXK2 at much higher levels under these conditions (Figure S3). S. kudriavzevii strains also express HXK1 at higher levels, although HXK2 is also significantly expressed (Figure S3).

We subsequently determined and compared the temperature profiles of total hexokinase activity in S. cerevisiae, S. kudriavzevii and S. uvarum (Figure 5). Given the different patterns of expression of the HXK1 and HXK2 genes in the three species, this activity may correspond in S. kudriavzevii to, at least, Hxk1p and Hxk2p, while in S. cerevisiae it is attributable only to Hxk2p. We did not determine the pattern of expression of Glk1p in S. kudriavzevii and S. uvarum, therefore this enzyme may also contribute to the total activity in these two species. In S. cerevisiae, GLK1 is not expressed in the presence of high glucose concentrations as employed in our experiments [58]. Between two and four strains were tested per species, to be able to distinguish trends associated with each species from strain-specific effects. Hexokinase activity was measured in total protein extracts and absolute activity levels were found to vary, sometimes considerably, among strains of the same species. The most striking variation was observed for S. cerevisiae reference strain S288C, which exhibited strongly elevated activities of all glycolytic enzymes tested when compared with all the other strains of the same species (data not shown). Curiously, S288C has been previously shown to exhibit particular and aberrant phenotypes when compared with other S. cerevisiae strains which were attributed to differences at the level of transcriptional regulation [61]. In general, we found absolute activities measured in crude extracts to be considerably variable among individual strains of the same species. Therefore, instead of absolute activity levels, we used relative hexokinase activities (for each strain all measurements refer to the highest activity measured) to construct the profiles shown in Figure 5, thereby facilitating comparisons between species.

Figure 5. Thermal profile of hexokinase activity in different species.

Relative total hexokinase activity measured in S. cerevisiae, S. kudriavzevii and S. uvarum at different temperatures. Activities were measured for each extract at the complete range of temperatures in a single experiment. Each point is the mean value obtained for a species at a given temperature, involving multiple strains and experiments. For each species profile, the highest relative activity measured was set at 100%.

Like observed for the glycolytic flux, it was possible to highlight species-specific trends in temperature profiles of hexokinase activity (Figure 5). The most prominent species-specific difference occurs at the lower temperature ranges, where S. kudriavzevii exhibits a much better performance than both S. cerevisiae and S. uvarum hexokinases. However, in S. kudriavzevii the temperature at which maximum activity is observed (25°C) is much lower than for S. cerevisiae and S. uvarum. For S. kudriavzevii, the higher activities observed at 10°C are in line with the increased glycolytic flux at this temperature but the observed decrease in thermal stability of hexokinase activity in this species does not seem to have a strong effect on the glycolytic flux at 30° and 35°C (Figure 4). This could mean that under these conditions hexokinase activity is not limiting the glycolytic flux.

The activity profiles in S. cerevisiae and S. uvarum differ at the highest temperatures, albeit only slightly, suggesting that the S. uvarum hexokinase activity could be more heat sensitive. To test this possibility, a series of assays were performed at 25°C, after pre-incubating the protein extracts at a range of higher temperatures for a short period of time. The results suggest that hexokinase activity in S. uvarum is less heat-resistant than in S. cerevisiae (Figure 6).

Figure 6. Thermal stability of hexokinase activity in S. cerevisiae and S. uvarum.

Total relative residual hexokinase activity measured in S.cerevisiae and S. uvarum after preincubation of crude extracts at increasingly higher temperatures for 5 minutes. Points represent mean values. The highest relative activity measured for each strain was set at 100%.

We also determined the temperature profile of the activity of the enzyme that catalyses the second step in glycolysis, phosphoglucoisomerase (PGI; Figure S4). Again, PGI relative activity was higher at the lower temperature range in the S. kudriavzevii strain but no loss of activity was observed at higher temperatures. The profiles for the S. uvarum and S. cerevisiae enzymes were very similar.

Evidence for divergent selection

Our results support the hypothesis that adaptation to different growth temperatures is likely to have undergone divergent selection along the various Saccharomyces lineages. Therefore, it seems plausible that adaptation to different temperature regimes promoted ecological speciation in this genus. In the course of our analysis we found several arguments that favor this possibility. Firstly, genes previously identified as being involved in the adaptation to suboptimal temperatures were found to be among the genes that diverged the most between cryotolerant and thermotolerant species, which favors the possibility that divergent selection determined their evolution pattern. Striking examples of this are the TIR1 and TIR4 genes encoding cell wall mannoproteins, which are induced by cold shock [52] and other involve the ECD1 and ECD2 genes encoding proteins important for translation during persistent heat stress [62]. Translation elongation factors and protein components of the ribosomal stalk whose role is to recruit the former to the ribosome, are also significantly overrepresented in the group of accelerated genes. Notably, the thermal stability of interactions at the level of the ribosomal stalk have been shown to correlate with growth temperature preferences in bacteria [63], suggesting a requirement for adaptation of this structure to be able to function well at different temperatures. Secondly, the glycolytic pathway, which lighted up under the premises of our analyses as likely to have undergone some form of divergent evolution, was found to exhibit species-specific trends that matched the growth temperature preferences of the species studied. While examining the glucose phosphorilation step in more detail, we observed that the paralogous genes HXK2 and HXK1 were very differently regulated in the three species studied, suggesting that the preferred expression of HXK2 at high glucose concentrations appeared in the lineage leading to S. cerevisiae. In this respect, it would be interesting to investigate whether Hxk2p might be the most heat stable of the two enzymes. If this was the case, inversion of the expression pattern might have constituted another contribution to improve the performance of this step of glycolysis at higher temperatures. However, it is also important to note that Hxk2p has a fundamental regulatory role in the main glucose repression pathway in addition to its role in glycolysis [54]. In S. cerevisiae this role is specific for Hxk2p, since Hxk1p is able to take over the regulatory role of Hxk2p only when overexpressed [64]. This means that there is some incipient subfunctionalization between the paralogues which is likely to be relevant for the evolution of the two genes. Presumably, Hxk1p is performing the regulatory function in S. uvarum where the main glucose repression pathway is thought to operate similarly to S. cerevisiae.

S. kudriavzevii and S. uvarum: different strategies for cold tolerance

Our in silico analysis was focused on the cryotolerant / thermotorelant pair S. uvarum/S. cerevisiae because the quality of the available sequences is much better for S. uvarum than for S. kudriavzevii, the other cryotolerant species [41], [42]. However, a more focused analysis concerning only glycolytic genes showed that in the comparison involving S. kudriavzevii, several glycolytic genes exhibited significantly higher dN/dS ratios (Table S3). Regardless of this, it should be noted, that the cryotolerant phenotypes of S. uvarum and S. kudriavzevii seem to be either the result of convergent evolution or of persistence in both species of an ancestral preference for growth at lower temperatures. In fact, it is consensual that the two species are not phylogenetically close [47] and we showed that carbon and energy metabolism respond very differently to temperature variations in the two species. While S. uvarum, a species that is closer to the root of the Saccharomyces tree (Figure 1), exhibits improved respiration rates at low temperatures, S. kudriavzevii seems to have specialized in ethanol production in similar conditions. Also, the glycolytic genes examined are not more similar between S. uvarum and S. kudriavzevii than in the comparison of each species with S. cerevisiae.

A hypothesis for the evolution of growth temperature preferences in Saccharomyces

In S. cerevisiae, there seems to be no clear rate limiting step in the conversion of intracellular glucose to pyruvate [65]. Therefore, the control over the glycolytic flux is in most conditions considered to be shared by all the enzymes. We found that approximately half of the glycolytic genes displayed accelerated dN/dS ratios when species with dissimilar growth temperature preferences were compared. We envisage that the glycolytic genes for which such accelerated evolution is not observed may encode enzymes whose eventual loss of performance at suboptimal temperatures did not compromise the total glycolytic flux (and fitness) in the course of evolution, because they did not become rate limiting upon that loss of activity. Taken together, and superimposed with the phylogenetic relationships depicted in Figure 1, our results support the view that the ancestral Saccharomyces phenotype may have resembled that of S. uvarum in what concerns the performance of glycolysis. This view is also supported by a recent study examining evolution of growth temperature preferences in Saccharomyces employing different approaches [66]. The lineage leading to S. cerevisiae seems to have evolved towards a better performance of the glycolytic pathway at higher temperatures denoted by higher fluxes than the other species above 30°C and a slightly improved thermal resistance of hexokinase activity. Conversely, the lineage leading to S. kudriavzevii evolved a glycolytic pathway that performs clearly better at low temperatures, which seems to have occurred at the cost of heat resistance, at least for hexokinase activity. In addition, the emphasis in this species seems clearly to be on ethanol production, which may be of ecological significance.

The in silico approach in the present work was guided by a specific working hypothesis concerning the role of growth temperature preference in the evolution of Saccharomyces, instead of being guided by phylogeny, like other approaches previously undertaken by others [44]. In short, an approach based on phylogeny might hopefully help to find answers to the broad question of which traits were under adaptive selection during the evolution of Saccharomyces species. The simpler, pairwise approach we chose aims to address the specific question of whether we can find evidence to support the hypothesis that growth temperature was an important trait during Saccharomyces evolution. It is likely that several relevant gene categories may have been missed in our analyses mainly because elevated dN/dS ratios may be confined to a functionally highly significant region of the gene that is nevertheless too short to impart a higher than average global dN/dS ratio to the entire gene. Growth temperature preference is known to be a highly complex phenotype [67]. Therefore, we anticipate that complementary experimental and in silico approaches will be able to uncover more genes related to this trait, thereby facilitating investigation of its relevance in a likely ecological speciation scenario in Saccharomyces.

Materials and Methods


The ORFeomes for S. cerevisiae, S. uvarum, S. kudriavzevii and S. mikatae were obtained as multi FASTA files from the SGD ( FTP server. Annotations of different quality resulted in the retrieval of different numbers of ORFs for the various species: S. cerevisiae, 6718 ORF; S. paradoxus, 6372 ORF; S. mikatae, 6228 ORF; S. kudriavzevii, 5785 ORF; S. uvarum, 6185 ORF. A small number of S. kudriavzevii ORFs was discarded during the analysis due to the presence of intervening STOP codons. Pairs of ORFeomes were subsequently chosen and subjected to bi-directional BLAST [68] in order to assess every homologous ORF according to our parameters. As a filter, rather than a specific e-value threshold, we relied on the extension of the global alignment. Bi-directional BLAST was carried out using the BiDiBlast Java pipeline [49]. The pipeline chains the reciprocal Blast with the refinement of every hit through global alignment of the putative homologous ORFs. Only ORFs aligning all their extension within 10% tolerance were retained as valid homologues. Reciprocal hits were tentatively classified as orthologs, and unidirectional hits were assigned to paralogs. The global alignment served also to infer the degree of similitude between homologous ORFs. The BiDiBlast pipeline calculates dN/dS ratios [69] over a codon-wise alignment of the matched ORFs. The complete set of results was uploaded into a relational database, and queries were built either to filter the data or to explore the relationships of interest.

The functional assignment of genes exhibiting unusually high dN/dS ratios in the S. uvarum/S. cerevisiae comparison, when compared with the S. paradoxus/S. cerevisiae comparison were manually purged from residual artifacts resulting from incompleteness or poor quality of the sequences and the functional/structural categories significantly enriched were essentially manually identified. Functional/structural groups “Ribosomal stalk proteins”, “Translation elongation factors” and “Cell wall mannoproteins” were manually assembled based on gene descriptions at SGD ; the category “glycolysis” was defined as in Conant & Wolfe [39]. The genes included in each category are listed in Table S2. The SGD Gene Ontology Slim Mapper was subsequently used to help highlight functional categories that might be enriched in the outlier group but had escaped the manual examination, but no additional functional categories were identified. Binomial tests( were performed to assess statistical significance of the enrichment of functional categories considering that the total number of ORFs retained in the analysis was 3583 and the number of outliers, defined as points significantly different from the main trend with a higher dN/dS ratio in the S. uvarum/S. cerevisiae comparison was 359. In order to detect outliers significantly different from the main trend the standard error about the linear regression parameters was used. Because the trend was very well defined, and the amount of points was very large, the estimate for the uncertainty about the regression line was low. We considered that this estimate was not useful because the observed dispersion of the points around the line was not even, and it seemed to grow along that same line. In order to account for this effect we added this trend to the uncertainty derived from the linear regression parameters. Moreover, the residual distribution parameters were affected by the t Student value when the significance level is 0.05. Thus, the resulting dashed lines in Figure 3 provide a stringent criterion to define the outlier group.

Competition assays

Two pairs of sympratic strains: Saccharomyces kudriavzevii ZP 591 vs S. cerevisiae ZP 567 and S. uvarum ZP 555 vs S. paradoxus ZP 551 were used to perform competition assays in batch cultures at different temperatures (5, 10, 15, 20, 27 and 30°C). The strains were inoculated separately in YNB with 1% glucose and incubated at the given temperature. At the beginning of the exponential growth phase, the two cultures were mixed in approximately equal proportions and a sample of the mixture was immediately harvested for DNA isolation (initial sample). The mixed cultures were subsequently incubated at the set temperature and harvested at stationary phase (final sample). Genomic DNA was extracted from initial and final samples as previously described by Soni & Murray [70], with a few modifications. Briefly: cells were harvested and resuspended in 500 µl lysis buffer (10 mM Tris-HCl pH 8.0, 100 mM NaCl, 1 mM EDTA (pH 8.0), 2% Trinton X-100, 1% SDS). An equal volume of phenol/chloroform (1∶1 v/v) was subsequently added and the whole mixture was vigorously shaken on a Ika-Vibrax VXR shaker at 1800 rpm for 20 minutes, at room temperature and centrifuged at 14000 rpm for 20 minutes at 4°C. Nucleic acids were precipitated with ethanol. The relative proportions of the competing strains in the initial and final samples was determined by qRT- PCR using the TAQurateTM GREEN Real-Time PCR MasterMix (Epicentre), each 12.5 µl reaction containing 0.1 µM of each primer (Table S1B) and 50 ng genomic DNA. Amplification consisted of an initial denaturation step at 94°C for 3 min, followed by 40 cycles of 30 s at 94°C, 30 s at variable annealing temperature (see Table S1B) and 30 s at 72°C. PCR was performed in a lightcycler Rotor-Gene 6000 apparatus (Qiagen, Germany). Reactions were performed in triplicate. The efficiency of the four PCR reactions was determined using pure genomic DNA of each of the competing strains and was used to calculate the relative amount of genomic DNA of each competing strain in the initial and final samples of each culture.

Quantification of HXK1 and HXK2 expression using qRT-PCR

HXK1 or HXK2 primers (Table S1C) allowed specific amplification of regions of the HXK1 and HXK2 spanning part of the coding regions in all the Saccharomyces species under study. Total RNA was isolated using TRIzol® (Invitrogen, Carlsbad, USA) according to the manufacturer’s instructions. The cultures were grown in 100 ml YPD (1% yeast extract, 2% peptone, 2% glucose) at 25°C , 150 rpm until O.D.640 nm 0.8 and were collected and frozen in liquid nitrogen. RNA samples were subsequently treated with DNaseI (Qiagen, Germany) and submitted to a conventional PCR reaction using the HXK1 and HXK2 primer pairs, to check for the absence of genomic DNA contamination. qReal-Time RT-PCR reactions were performed with MasterAmpTM GREEN Real-Time RT-PCR Kit (Epicentre), each 12.5 µl reaction containing 0.5 mM MnSO4, 0.1 µM of each primer (Table S1C), 40 ng/reaction of purified RNA and 2.5U RetroAmp RT DNA polymerase Amplification consisted of an initial RT- step at 60°C for 30 min, followed by a denaturation step at 94°C for 3 min, then by 40 cycles of 30 s at 94°C, 30 s at 49°C and 30 s at 72°C. For each RNA sample at least two independent qReal-Time RT-PCR experiments were carried out, usually in triplicate. The efficiency of each reaction was calculated using genomic DNA and were used to determine the relative amounts of HXK1 and HXK2 mRNAs.

Crude extracts for enzymatic assays

The cultures were grown in YPD (1% yeast extract, 2% peptone, 2% glucose) at 25°C, 150 rpm until mid exponential phase and were harvested by centrifugation (4°C, 10 min at 9000 rpm), washed twice with phosphate buffer (10 mM potassium phosphate, 2 mM EDTA pH 7.5), concentrated fourfold, and stored at −80°C. Before assaying, the cells were thawed, washed and resuspended in 400 µl of phosphate buffer with 2 mM MgCl2 and 1 mM DTT. Lysis buffer (0.1 M potassium phosphate, 2 mM MgCl2 and 1 mM DTT) and 200 µl glass beads (∅ 230–320 nm) were subsequently added to the suspension. The cells were disrupted by six alternating cycles of 1 min vortexing followed by 1 min cooling on ice. Cell debris were removed by centrifugation (4°C, 20 min at 13000 rpm), and the supernatant was immediately used for enzymatic assays. Total protein concentration in the crude extracts was determined using the BCA protein assay kit (PIERCE) with bovine serum albumin as a standard.

Enzymatic assays

Enzymatic assays were always performed using freshly prepared crude extracts of the following strains: S. cerevisiae, S288C; CEN.PK113-11C, ZP 567 and ZP 736; S. kudriavzevii, IFO 1802, ZP 591, ZP 513 and ZP 828; S.uvarum, CBS 7001, ZP 555 and ZP 663.

Hexokinase (HXK; EC was assayed in 5 mM glucose, 1 mM ATP, 0.25 mM NADP+, and 0.54 unit of glucose-6-P dehydrogenase and phosphoglucose isomerase (PGI; EC in 1 mM fructose-6-P, 0.05 mM NADP+, and 0.27 unit of glucose-6-P dehydrogenase, both in 1 ml reaction volume. The rate of the reactions was determined by measuring the absorbance at 340 nm using an UV-2101PC thermostatized spectrophotometer (Shimadzu, Japan) for 1 minute after the reaction was started. The reactions were always started by the addition of sugars, after pre-incubation of the extract at the assay temperature for 10 sec. All assays were performed using three different amounts of cell extracts (5; 7.5 and 12.5 µg protein).

To determine the relative thermal stability of hexokinase activity in S. uvarum and S. cerevisiae, a sample of the crude extract was pre-incubated at 40, 42, 44, 46, 48 and 50°C for 5 min, and immediately cooled on ice. The amount of residual enzyme activity was subsequently assayed at 25°C.

Warburg Assays

Cultures were grown in 100 ml YNB with 2% glucose at 25°C, 150 rpm until mid exponential phase, harvested by centrifugation (4°C, 10 min at 9000 rpm), washed twice with phosphate buffer (100 mM potassium phosphate, pH 4.5), and resuspended in the appropriate volume of buffer to a final concentration of approximately 1 mg dW/ml.

Glucose consumption was evaluated in resting cells using a Warburg volumetric apparatus (B. Braun Melsungen), which allows the quantification of CO2 produced and O2 consumed [57]. At least two different cultures were performed for each strain and each measurement was performed in duplicate. The complete set of results is summarized in Table S4. Statistical significance of the differences observed between datasets was assessed using an unpaired T-test (, reported p values are two tailed.

Supporting Information

Figure S1.

Outcome of competition between sympatric Saccharomyces species. Relative fitness of S. paradoxus ZP 551 competing with S. uvarum ZP 555 at different temperatures is shown. The relative size of the populations of both species was determined in the beginning and at the end of each co-culture experiment by qRT-PCR using species specific primers and was used to calculate the relative fitness of each species.


Figure S2.

Comparison of dN/dS ratios of Saccharomyces orthologous gene pairs in different species comparisons. dN/dS ratios depicted were determined between S. uvarum and S. cerevisiae (Yaxis) and between S. paradoxus and S. cerevisiae (Xaxis). Each point corresponds to one orthologous gene pair that could be reliably identified in both species comparisons. The regression line is shown in solid blue. The dashed lines add the contribution of the residuals distribution. Points outside these lines may be considered as significantly different from the general trend. The group of orthologs of interest to the present analysis displays higher dN/dS ratios in the comparison between the S. uvarum and S. cerevisiae genomes than in the comparison between S. paradoxus and S. cerevisiae, corresponding therefore to data points located above the segment defined by the dashed lines. Points representing glycolytic genes are highlighted as red circles. Primary data used to construct the plot is listed in Table S2.


Figure S3.

Relative expression of the HXK1 (grey) and HXK2 (black) genes determined by RT-Real Time PCR in six strains belonging to Saccharomyces cerevisiae, S. uvarum and S. kudriavzevii, as indicated.


Figure S4.

Thermal profile of phosphoglucose isomerase (PGI) activity in different species. Relative total phosphoglucose isomerase activity measured in S. cerevisiae CEN.PK-113-11C, S. kudriavzevii IFO 1802T and S. uvarum CBS 7001 at different temperatures. Activities were measured for each extract at the complete range of temperatures in a single experiment. Each point is the mean value of the data obtained for each strain at the given temperature. For each strain profile, the highest relative activity measured was set at 100%.


Table S1.

A. List of strains used in this study. B. Primers and annealing temperatures used for Real-Time PCR analyses in competition assays (Fig. 2 and Fig. S1). C. Primers and annealing temperatures used for RT-Real-Time PCR analyses of the relative expression of the HXK1 and HXK2 genes (Fig. S3).


Table S2.

dN, dS and dN/dS ratios determined for Saccharomyces ORFeomes between S. paradoxus and S. cerevisiae and between S. uvarum and S. cerevisiae.


Table S3.

dN/dS ratios determined for glycolytic genes in different Saccharomyces species comparisons.


Table S4.

Glycolytic and respiratory fluxes measured in Warburg assays.


Author Contributions

Conceived and designed the experiments: PG JPS. Performed the experiments: EV PG CC. Analyzed the data: PG JPS EV. Contributed reagents/materials/analysis tools: JPS JMGCFdA. Wrote the paper: PG JPS EV JMGCFdA.


  1. 1. Kurtzman CP (2003) Phylogenetic circumscription of Saccharomyces, Kluyveromyces and other members of the Saccharomycetaceae, and the proposal of the new genera Lachancea, Nakaseomyces, Naumovia, Vanderwaltozyma and Zygotorulaspora. FEMS Yeast Res 4: 233–425.
  2. 2. Nguyen HV, Gaillardin C (2005) Evolutionary relationships between the former species Saccharomyces uvarum and the hybrids Saccharomyces bayanus and Saccharomyces pastorianus; reinstatement of Saccharomyces uvarum (Beijerinck) as a distinct species. FEMS Yeast Res 5: 471–483.
  3. 3. Wang SA, Bai FY (2008) Saccharomyces arboricolus sp. nov., a yeast species from tree bark. Int J Syst Evol Microbiol 58: 510–514.
  4. 4. Martini A (1993) Origin and domestication of the wine yeast Saccharomyces cerevisiae. J Wine Res 4: 165–176.
  5. 5. Vaughan-Martini A, Martini A (1995) Facts, myths and legends of the prime industrial microorganism. J Ind Microbiol 14: 514–522.
  6. 6. Ciani M, Mannazzu I, Marinangeli P, Clementi F, Martini A (2004) Contribution of winery-resident Saccharomyces cerevisiae strains to spontaneous grape must fermentation. Antonie van Leeuwenhoek 85: 159–164.
  7. 7. Cavalieri D, McGovern PE, Hartl DL, Mortimer R, Polsinelli M (2003) Evidence for S. cerevisiae Fermentation in Ancient Wine. J Mol Evol 57: S226–S232.
  8. 8. McGovern PE, Zhang J, Tang J, Zhang Z, Hall GR, et al. (2004) Fermented beverages of pre- and proto-historic China. Proc Natl Acad Sci USA 101: 17593–17598.
  9. 9. Rainieri S, Kodama Y, Kaneko Y, Mikata K, Nakao Y, et al. (2006) Pure and mixed genetic lines of Saccharomyces bayanus and Saccharomyces pastorianus and their contribution to the lager brewing strain genome. Appl Environ Microbiol 72: 3968–3974.
  10. 10. Fernández-Espinar MT, Barrio E, Querol A (2003) Analysis of the genetic variability in the species of the Saccharomyces sensu stricto complex. Yeast 20: 1213–1226.
  11. 11. Rainieri S, Zambonelli C, Hallsworth JE, Pulvirenti A, Giudici P (1999) Saccharomyces uvarum, a distinct group within Saccharomyces sensu stricto. FEMS Microbiol Lett 177: 177–185.
  12. 12. Bond U (2009) The genomes of lager yeasts. In: Laskin AI, Sariaslani S, Gadd GM, editors. Advances in Applied Microbiology. Burlington: Academic Press. pp. 159–182.
  13. 13. Giudici P, Caggia C, Pulvirenti A, Zambonelli C, Rainieri S (1998) Electrophoretic profile of hybrids between cryotolerant and non-cryotolerant Saccharomyces strains. Lett Appl Microbiol 27: 31–34.
  14. 14. Sniegowski PD, Dombrowski PG, Fingerman E (2002) Saccharomyces cerevisiae and Saccharomyces paradoxus coexist in a natural woodland site in North America and display different levels of reproductive isolation from European conspecifics. FEMS Yeast Res 1: 299–306.
  15. 15. Fay JC, Benavides JA (2005) Evidence for domesticated and wild populations of Saccharomyces cerevisiae. PLoS Genet 1: 66–71.
  16. 16. Replansky T, Koufopanou V, Greig D, Bell G (2008) Saccharomyces sensu stricto as a model system for evolution and ecology. Trends Ecol Evol 23: 494–501.
  17. 17. Aa E, Townsend JP, Adams RI, Nielsen KM, Taylor JW (2006) Population structure and gene evolution in Saccharomyces cerevisiae. FEMS Yeast Res 6: 702–715.
  18. 18. Legras J, Merdinoglu D, Cornuet J, Karst F (2007) Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history. Mol Ecol 16: 2091–2102.
  19. 19. Diezmann S, Dietrich F (2009) Saccharomyces cerevisiae: population divergence and resistance to oxidative stress in clinical, domesticated and wild isolates. PLoS One 4: e5317.
  20. 20. Tsai IJ, Bensasson D, Burt A, Koufopanou V (2008) Population genomics of the wild yeast Saccharomyces paradoxus: Quantifying the life cycle. Proc Natl Acad Sci USA 105: 4957–4962.
  21. 21. Koufopanou V, Hughes J, Bell G, Burt A (2006) The spatial scale of genetic differentiation in a model organism: the wild yeast Saccharomyces paradoxus. Philos Trans R Soc Lond B Biol Sci 361: 1941–1946.
  22. 22. Kuehne HA, Murphy HA, Francis CA, Sniegowski PD (2007) Allopatric divergence, secondary contact, and genetic isolation in wild yeast populations. Curr Biol 17: 407–411.
  23. 23. Liti G, Barton DBH, Louis EJ (2006) Sequence diversity, reproductive isolation and species concepts in Saccharomyces. Genetics 174: 839–850.
  24. 24. Liti G, Carter DM, Moses AM, Warringer J, Parts L, et al. (2009) Population genomics of domestic and wild yeasts. Nature 458: 337–341.
  25. 25. Sampaio JP, Gonçalves P (2008) Natural populations of Saccharomyces kudriavzevii in Portugal are associated with oak bark and are sympatric with S. cerevisiae and S. paradoxus. Appl Environ Microbiol 74: 2144–2152.
  26. 26. Lopes C, Barrio E, Querol A (2010) Natural hybrids of S. cerevisiae x S. kudriavzevii share alleles with European wild populations of Saccharomyces kudriavzevii. FEMS Yeast Res 10: 412–421.
  27. 27. Sweeney J, Kuehne H, Sniegowski P (2004) Sympatric natural Saccharomyces cerevisiae and S. paradoxus populations have different thermal growth profiles. FEMS Yeast Res 4: 521–525.
  28. 28. Rundle HD, Nosil P (2005) Ecological speciation. Ecol Lett 8: 336–352.
  29. 29. Merico A, Sulo P, Piskur J, Compagno C (2007) Fermentative lifestyle in yeasts belonging to the Saccharomyces complex. FEBS J 274: 976–989.
  30. 30. van Dijken J, Weusthuis R, Pronk J (1993) Kinetics of growth and sugar consumption in yeasts. Ant Leeuw 63: 343–352.
  31. 31. Wolfe KH, Shields DC (1997) Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387: 708–713.
  32. 32. Kellis M, Birren B, Lander E (2004) Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae. Nature 428: 617–624.
  33. 33. Byrne K, Wolfe K (2007) Consistent patterns of rate asymmetry and gene loss indicate widespread neofunctionalization of yeast genes after whole-genome duplication. Genetics 175: 1341–1350.
  34. 34. van Hoek M, Hogeweg P (2009) Metabolic adaptation after whole genome duplication. Mol Biol Evol 26: 2441–2453.
  35. 35. Scannell D, Byrne K, Gordon J, Wong S, Wolfe K (2006) Multiple rounds of speciation associated with reciprocal gene loss in polyploid yeasts. Nature 440: 341–345.
  36. 36. Scannell DR, Wolfe KH (2008) A burst of protein sequence evolution and a prolonged period of asymmetric evolution follow gene duplication in yeast. Genome Res 18: 137–147.
  37. 37. Hittinger C, Carroll S (2007) Gene duplication and the adaptive evolution of a classic genetic switch. Nature 449: 677–681.
  38. 38. Cliften P, Fulton R, Wilson R, Johnston M (2006) After the duplication: gene loss and adaptation in Saccharomyces genomes. Genetics 172: 863–872.
  39. 39. Conant GC, Wolfe KH (2007) Increased glycolytic flux as an outcome of whole-genome duplication in yeast. Mol Syst Biol 3: 1–12.
  40. 40. Piskur J, Rozpedowska E, Polakova S, Merico A, Compagno C (2006) How did Saccharomyces evolve to become a good brewer? Trends Genet 22: 183–186.
  41. 41. Kellis M, Patterson N, Endrizzi M, Birren B, Lander E (2003) Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423: 241–254.
  42. 42. Cliften P, Sudarsanam P, Desikan A, Fulton L, Fulton B, et al. (2003) Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 301: 71–76.
  43. 43. Sawyer SL, Malik HS (2006) Positive selection of yeast nonhomologous end-joining genes and a retrotransposon conflict hypothesis. Proc Natl Acad Sci USA 103: 17614–17619.
  44. 44. Kawahara Y, Imanishi T (2007) A genome-wide survey of changes in protein evolutionary rates across four closely related species of Saccharomyces sensu stricto group. BMC Evol Biol 7: 9.
  45. 45. Li Y, Liang H, Gu Z, Lin Z, Guan W, et al. (2009) Detecting positive selection in the budding yeast genome. J Evol Biol 22: 2430–2437.
  46. 46. Mariño-Ramírez L, Bodenreider O, Kantz N, Jordan I (2006) Co-evolutionary rates of functionally related yeast genes. Evol Bioinform Online 2: 295–300.
  47. 47. Rokas A, Williams BL, King N, Carroll SB (2003) Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature 425: 798–804.
  48. 48. Ganter PF, Starmer WT (1992) Killer factor as a mechanism of interference competition in yeasts associated with cacti. Ecology 73: 54–67.
  49. 49. de Almeida JMGCF (2010) BiDiBlast: comparative genomics pipeline for the PC. Genomics, Proteomics & Bioinformatics 8: 135–138.
  50. 50. Linder P (2003) Yeast RNA helicases of the DEAD-box family involved in translation initiation. Biol Cell 95: 157–167.
  51. 51. Homma T, Iwahashi H, Komatsu Y (2003) Yeast gene expression during growth at low temperature. Cryobiol 46: 230–237.
  52. 52. Abe F (2007) Induction of DAN/TIR yeast cell wall mannoprotein genes in response to high hydrostatic pressure and low temperature. FEBS Lett 581: 4993–4998.
  53. 53. Tai S, Daran-Lapujade P, Luttik M, Walsh M, Diderich J, et al. (2007) Control of the glycolytic flux in Saccharomyces cerevisiae grown at low temperature: a multi-level analysis in anaerobic chemostat cultures. J Biol Chem 282: 10243–10251.
  54. 54. Gancedo JM (2008) The early steps of glucose signalling in yeast. FEMS Microbiol Rev 32: 673–704.
  55. 55. López-Villar E, Monteoliva L, Larsen MR, Sachon E, Shabaz M, et al. (2006) Genetic and proteomic evidences support the localization of yeast enolase in the cell surface. Proteomics 6: S107–118.
  56. 56. Motshwene P, Brandt W, Lindsey G (2003) Significant quantities of the glycolytic enzyme phosphoglycerate mutase are present in the cell wall of yeast Saccharomyces cerevisiae. Biochem J 369: 357–362.
  57. 57. Umbreit W, Burris R, Stauffer J (1964) Manometric Techniques. Minneapolis: Burgess Publishing Co.
  58. 58. Herrero P, Galíndez J, Ruiz N, Martínez-Campa C, Moreno F (1995) Transcriptional regulation of the Saccharomyces cerevisiae HXK1, HXK2 and GLK1 genes. Yeast 11: 137–144.
  59. 59. Rodríguez A, De La Cera T, Herrero P, Moreno F (2001) The hexokinase 2 protein regulates the expression of the GLK1, HXK1 and HXK2 genes of Saccharomyces cerevisiae. Biochem J 355: 625–631.
  60. 60. De Winde JH, Crauwels M, Hohmann S, Thevelein JM, Winderickx J (1996) Differential requirement of the yeast sugar kinases for sugar sensing in establishing the catabolite-repressed state. Eur J Biochem 241: 633–643.
  61. 61. Kvitek D, Will J, Gasch A (2008) Variations in stress sensitivity and genomic expression in diverse S. cerevisiae isolates. PLoS Genet 4: e1000223.
  62. 62. Neef DW, Thiele DJ (2009) Enhancer of decapping proteins 1 and 2 are important for translation during heat stress in Saccharomyces cerevisiae. Microbiol 73: 1032–1042.
  63. 63. Shcherbakov D, Dontsova M, Tribus M, Garber M, Piendl W (2006) Stability of the 'L12 stalk' in ribosomes from mesophilic and (hyper)thermophilic Archaea and Bacteria. Nucleic Acids Res 34: 5800–5814.
  64. 64. Ma H, Botstein D (1986) Effects of null mutations in the hexokinase genes of Saccharomyces cerevisiae on catabolite repression. Mol Cell Biol 6: 4046–4052.
  65. 65. Reijenga K, Snoep J, Diderich J, van Verseveld H, Westerhoff H, et al. (2001) Control of glycolytic dynamics by hexose transport in Saccharomyces cerevisiae. Biophys J 80: 626–634.
  66. 66. Salvadó Z, Arroyo-López FN, Guillamón JM, Salazar G, Querol A, et al. (2011) Temperature adaptation markedly determines evolution within the genus Saccharomyces. Appl Environ Microbiol 77: 2292–2302.
  67. 67. Parts L, Cubillos F, Warringer J, Jain K, Salinas F, et al. (2011) Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res.
  68. 68. Rivera MC, Jain R, Moore JE, Lake JA (1998) Genomic evidence for two functionally distinct gene classes. Proc Natl Acad Sci USA 95: 6239–6244.
  69. 69. Yang Z, Nielsen R (2000) Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17: 32–43.
  70. 70. Soni R, Murray JA (1992) A rapid and inexpensive method for isolation of shuttle vector DNA from yeast for the transformation of E.coli. Nucleic Acids Res 20: 5852.