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. 2020 May 2;37(9):2520–2530. doi: 10.1093/molbev/msaa101

Pervasive Phenotypic Impact of a Large Nonrecombining Introgressed Region in Yeast

Christian Brion m1, Claudia Caradec m1, David Pflieger m1, Anne Friedrich m1, Joseph Schacherer m1,m2,✉
Editor: Patricia Wittkopp
PMCID: PMC7475044  PMID: 32359150

Abstract

To explore the origin of the diversity observed in natural populations, many studies have investigated the relationship between genotype and phenotype. In yeast species, especially in Saccharomyces cerevisiae, these studies are mainly conducted using recombinant offspring derived from two genetically diverse isolates, allowing to define the phenotypic effect of genetic variants. However, large genomic variants such as interspecies introgressions are usually overlooked even if they are known to modify the genotype–phenotype relationship. To have a better insight into the overall phenotypic impact of introgressions, we took advantage of the presence of a 1-Mb introgressed region, which lacks recombination and contains the mating-type determinant in the Lachancea kluyveri budding yeast. By performing linkage mapping analyses in this species, we identified a total of 89 loci affecting growth fitness in a large number of conditions and 2,187 loci affecting gene expression mostly grouped into two major hotspots, one being the introgressed region carrying the mating-type locus. Because of the absence of recombination, our results highlight the presence of a sexual dimorphism in a budding yeast for the first time. Overall, by describing the phenotype–genotype relationship in the Lachancea kluyveri species, we expanded our knowledge on how genetic characteristics of large introgression events can affect the phenotypic landscape.

Keywords: genotype, phenotype, introgressions, QTL, sex chromosome, yeast

Introduction

Phenotypic diversity, such as variation of fitness, sensitivity to diseases, or differences of behavior, is intrinsically linked to genetic variation. Consequently, in health research, food industry, and many other biological fields, the genotype–phenotype relationship is deeply investigated to have a better understanding of the cause of this diversity and its evolution. Such relationship can be dissected by using large genotyped and phenotyped populations (1000 Genomes Project Consortium et al. 2010; UK10K Consortium et al. 2015; Alonso-Blanco et al. 2016; Peter et al. 2018). In yeast, and especially in the model species Saccharomyces cerevisiae, these populations are generally constituted of recombinant offspring originated from crosses, which prevent biases of the association analysis regarding population structure, or the effect of rare variants (Brem et al. 2002; Steinmetz et al. 2002; Fay 2013; Bloom et al. 2019; Fournier et al. 2019). These analyses allowed the identification of a large number of quantitative trait loci (QTL), responsible for the variation of a broad set of phenotypes such as the growth fitness in various conditions, cell morphology, or gene expression (RNA level, eQTL, and protein level, pQTL) (Brem et al. 2002; Nogami et al. 2007; Smith and Kruglyak 2008; Bloom et al. 2013; Fay 2013; Albert et al. 2014, 2018; Clément-Ziza et al. 2014; Peltier et al. 2019).

Most of these analyses mainly focused on identifying the effects of point mutations. However, other types of genetic variants have been shown to affect the phenotypic diversity and the dynamics of species evolution. One of these variants corresponds to the transfer of genetic material across species, such as introgression events. Such transfers have been identified in yeast and can confer phenotypic advantages in wine-making process (Novo et al. 2009; Marsit et al. 2015). In addition, it has been shown through the lens of the sequences of 1,011 S. cerevisiae genomes that these introgression events are common. In total, 885 introgressed genes coming from the Saccharomyces paradoxus sister species have been identified in this population (Peter et al. 2018). However, the overall impact of introgression events on the dynamic of evolution of yeast species remains to be explored.

Although most of the introgression events are usually small in S. cerevisiae, the preduplicated species Lachancea kluyveri carry a very large (∼1 Mb) genomic introgression, corresponding to the left arm of the chromosome C (Génolevures Consortium et al. 2009; Payen et al. 2009). This introgressed region is common to all the sequenced isolates of the species (Friedrich et al. 2015). It presents a higher GC content compared with the rest of the genome (53% vs. 40%) and displays different evolutionary features: The genetic diversity is higher (π = 0.019 vs. π = 0.017), the linkage disequilibrium is shorter (LD1/2 = 0.3 kb vs. LD1/2 = 1.5 kb), and the mutation types are unbalanced (Friedrich et al. 2015). Recently, a potential donor species, Eremothecium gossypii, has been suggested as the donor species, based on synteny and codon usage (Landerer, O’Meara, Zaretzki, and Gilchrist, unpublished data). We also observed that no double strand breaks occurred in this region during meiosis, preventing cross-overs and allelic shuffling (Brion et al. 2017). This latest feature was surprising as it is in contradiction with the lower LD previously observed, and this inconsistency is yet to be explained. The absence of recombination, associated with the fact that the region contains the mating-type locus (MAT locus), is particularly noteworthy. Indeed, the region around the MAT locus is generally associated with a diminution of the recombination rate in yeast, but it is usually restricted to a very small portion of the genome around that locus.

To have a global view of the genetic architecture of traits in L. kluyveri as well as to assess the phenotypic impact of this large nonrecombining introgressed region, we performed linkage mapping on large populations of segregants. Quantitative growth in 64 conditions, which induce various physiological and cellular responses, and gene expression variations were measured, leading to the determination of 89 QTL and 2,187 eQTL, mostly grouped into two common major hotspots. Interestingly, one of the QTL and eQTL hotspots corresponds to the introgressed region, highlighting the pervasive phenotypic impact of it. We also demonstrated an association between different traits and the mating-type locus due to the absence of recombination in the introgressed region. This observation implies a sexual differentiation in the species and consequently highlights the presence of a sexual dimorphism in a budding yeast for the first time. Finally, by providing an exhaustive view of the QTL and eQTL landscape, we were able to compare the genotype–phenotype maps across different yeast species.

Results

Lachancea kluyveri Introgressed Region Shows No Recombination after Two Cycles of Meiosis

In order to identify the genetic basis underlying the phenotypic diversity in L. kluyveri, we used a cross between the NBRC10955 (MATa), and 67-588 (MATα) strains, for which the density of genetic variant is around seven mutations per kilobase (0.7% of diversity). In our previous survey focusing on the recombination landscape of this species, we generated and sequenced 196 F1 offspring (from 45 full tetrads) (Brion et al. 2017). Here, we used 180 of these strains, plus two additional sequenced strains that do not come from a full tetrad. We genotyped this F1 population by defining the parental origins of 56,612 variants along the genome and identified the position as well as the number of recombination events per spore. These recombination events are the result of not only cross-overs during meiosis but also loss of heterozygosity, likely induced by incomplete meiosis and return to growth event prior the final meiosis (Brion et al. 2017). On average, 14.7 recombination events were detected by strains, ranging from 3 to 30 (supplementary fig. S1, Supplementary Material online), whereas none was observed in the introgressed region located on the left arm of the chromosome C.

Because the recombination rate in L. kluyveri is 3.7 time lower than that in S. cerevisiae (Brion et al. 2017), we generated a F2 population of recombinant strains from the same cross in order to perform an eQTL analysis. We crossed MATa and MATα strains from the F1 population (69 crosses). These hybrids were put on sporulation condition, and the resulting tetrads were dissected. The F2 population is composed of 50 segregants coming from 50 independent crosses and full tetrads (one spore per tetrad was selected). RNA sequencing (RNA-seq) of this F2 population allowed characterization of the parental allele inheritance for each segregant. By only using variants in coding frame, the number of used polymorphic sites was reduced to a total of 37,529. Because recombination is low, we used pseudomarkers every 3 kb to reduce the polymorphic sites to 3,779, which still allowed us to detect recombination accurately. Again, in this population, no recombination events were detected in the introgressed region, inducing perfect linkage across its variants. The average number of recombination events per spore in the F2 population was 19.5, ranging from 10 to 32 (supplementary fig. S1, Supplementary Material online). As expected, the number of recombination events was higher than in the F1 population, but not twice as much as undetected recombination events could occur in the homozygote regions of the F1 hybrids. Due to the lower recombination rate in L. kluyveri compared with S. cerevisiae, we observed larger blocks of linked variants along the genome (supplementary fig. S2, Supplementary Material online).

A Significant Part of the Heritable Phenotypic Variance Is Linked to the Mating-Type Locus

To explore the genetic basis of phenotypic diversity in L. kluyveri, we looked at the variation of fitness and expression in our F1 and F2 populations, respectively. In this study, we defined fitness as the individual growth capacity in a specific condition. The fitness across 64 various conditions was estimated by measuring the colony size of the 182 F1 strains growing on solid media. These conditions include temperature variation, various carbon and nitrogen sources, the presence of toxic compounds, and pH variation (supplementary tables S1 and S2, Supplementary Material online). From these data, we first estimated the phenotypic variance as well as the broad-sense heritability (H2), and we observed that most of the phenotype display a H2 superior to 70% (60 out of 64 tested conditions). This clearly indicates that the experimental error is low and most of the variation is due to heritable factors (fig. 1A). We then looked at the distribution of the traits in the population and almost all the phenotypes displayed a normal distribution, suggesting a complex genetic control (examples in fig. 1B). Only four phenotypes displayed a bimodal distribution, which could indicate a major locus control (6-azauracil 600 mg/l, anisomycine 50 mg/l, NaCl 1 M and 1.5 M) (fig. 1B). We also quantified the mRNA for 5,380 annotated genes across the 50 strains of the F2 population using the RNA-seq data and estimated the H2 for each gene using expression data from a previous experiment (see Materials and Methods). The expression of the majority of the genes (∼78%) showed a H2 >70%, indicating that most of gene expression variation is carried by heritable factors.

Fig. 1.

Fig. 1.

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Phenotypic diversity in segregating populations. (A) Distribution of the total variance of the growth ratio across the 63 conditions (YPD colonies sized are excluded), compared with the genetic variance and the variance explained by the best QTL. (B) Three examples of distribution of the growth ratio in the F1 population selected from (A) (indicated by the arrows), for growth in 0.2 M of CaCl2 (normal distribution), 1 M of NaCl (bimodal distribution), and Ureidopropionate as unique nitrogen source (unclear distribution with a large variance). Top of each plot is the dot clouds distribution of the phenotype in the population, where mean value of the segregants is represented by gray lines. Means across the replicates of the parental strains are NBRC10955 (blue), 67-588 (orange), and hybrid NBRC10955 × 67-588 (green). (C) PCA of the fitness measure for F1 population (left) and expression profiles of the F2 population (right). Both populations are separated according to the MAT genotype: MATa genotype (black) and MATα genotype (gray). Parental strains follow the same color code as (B).

We explored the phenotypic dispersion of the strains in both the F1 and the F2 population using principal component analyses (PCAs) (fig. 1C). The projection of the strains in the two PCAs showed that our data have no outliers with extreme phenotypes and that the parental strains are generally separated, that is, behaving differently. More importantly, we observed that for both PCAs, a component can separate the populations accordingly to their mating type: the second component (16.6%) for the fitness phenotypes in the F1 population, and the fourth component (6.12%) for gene expression in the F2 population (fig. 1C). This result clearly highlighted a link between the MAT locus and a large part of the phenotypic variance. This link was also observed in a clustering analysis, regrouping genes based on their expression profiles across segregants. As expected, clustered genes were involved in similar biological processes due to the link between regulatory network and biological function (supplementary fig. S3, Supplementary Material online). However, a cluster containing genes involved in the mating-type determination was also constituted of genes involved in other unrelated processes and was enriched in genes located in the introgressed region. Overall, the variation of fitness and expression was partly associated with the mating type, which suggested an important role of the introgression carrying the MAT locus in L. kluyveri diversity. Such link was confirmed by the following QTL analysis.

Majority of the Detected QTL in L. kluyveri Are Localized within Two Pleiotropic Loci

Using genotypic and phenotypic data of the F1 population, we identified loci that have an impact on fitness variance. The linkage analysis allowed detection of 89 QTL for 90% of the fitness measures (58 out of the 64 growth condition), with a false discovery rate (FDR) of 5%. The number of significant additive QTL detected per phenotype varies from 1 to 7 (fig. 2A and supplementary fig. S4 and table S3, Supplementary Material online). Similarly, in the F2 population, we linked gene expression variation to the genotype and detected 2,187 eQTL for 2,048 genes (about 34% of the genome), at a logarithm of odd (LOD score) threshold of 3.7 (FDR of ∼4%, supplementary fig. S5, Supplementary Material online). Only one eQTL was detected for most of the genes (1,911), whereas 135 genes have two eQTL and only two genes have three eQTL. Due to the low recombination rate in L. kluyveri, the loci identified are large, with a median of 70 kb, encompassing ∼25 genes (fig. 2B and supplementary fig. S6 and table S4, Supplementary Material online).

Fig. 2.

Fig. 2.

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Summary of the linkage analysis. (A) Density of fitness QTL (represented by gray bars) detected along the genome. The pie charts describe the two major QTL hotspots, indicating the number of growth conditions inducing different types of stresses. (B) Density of distant-eQTL (in black) and all eQTL (distant and local, in light blue) along the genome. The M indicates the position of the MAT locus.

We estimated the variance explained by each QTL and found that they explain a least 7.3% of the variance and only one case explain more than 60% (fig. 1A and supplementary fig. S7A, Supplementary Material online). Similarly, we calculated the variance of gene expression explained by each eQTL and observed that these values cannot be lower than 20%. This indicated a lower detection power for the eQTL compared with the fitness QTL, which can be explained by the smaller size of the F2 population. A major allelic effect was observed for 400 eQTL, explaining more than 60% of the expression variance (supplementary fig. S7B, Supplementary Material online). The only strong genetic control on fitness detected affects resistance to sodium chloride (NaCl, 1 M) and explains perfectly the bimodal distribution of the phenotype (61.4% of variance explained). This QTL is located on chromosome H and is due to a variation in the SAKL0H13222g gene, an ortholog of the S. cerevisiae ENA2 gene. This gene encodes a membrane sodium transporter and has already been shown to have an impact on sodium resistance in our cross (Sigwalt et al. 2016). Interestingly, the ENA2 allele also affects fitness in other media containing sodium (sodium dodecyl sulfate [SDS], sodium acetate). An eQTL explaining 94% of the expression variance of ENA2 is also located at this position, suggesting that the genetic variant acts through a modification of the expression of the ENA2 gene. Similarly to L. kluyveri, the modification of ENA2 expression, via copy number variation, also leads to the variation of salt tolerance across S. cerevisiae strains (Ruiz and Ariño 2007; Doniger et al. 2008). Using read coverage, we confirmed that the gene ENA2 is present in only one copy in both our parental strains, suggesting another mechanism involved in this eQTL.

We then examined the location of the 89 fitness QTL and 2,187 eQTL across the genome and found that a large number of these QTL are regrouping in only two genetic locations, that is, two hotspots (fig. 2). The first hotspot is located on the chromosome C, covering the introgressed region, and is composed of 23 fitness QTL and 247 eQTL. The second hotspot is on the chromosome E and contains 23 fitness QTL and 512 eQTL. The presence of QTL hotspots is a phenomenon observed in similar analyses in yeast, generally due to one genetic variant with pleiotropic effects, affecting multiple networks and traits. With the extreme detection power of more than 1,000 segregants, 9 fitness QTL and 102 eQTL hotspots were determined in S. cerevisiae, unveiling how frequent these pleiotropic variants are in this species (Bloom et al. 2013; Albert et al. 2018).

The Absence of Recombination Is Responsible for the Genetic Link between the MAT Locus and a Large Number of Traits

The overlap between the fitness and expression QTL hotspots and the introgressed locus demonstrates an important role of this region on the phenotypic diversity. Because of the absence of recombination, all these QTL cover the large 1-Mb region, comprising ∼500 genes. Therefore, it is difficult to know if these QTL are due to one pleiotropic causal variant or multiple genetically linked variants. However, our results are in favor of the latter hypothesis, as the fitness QTL detected affect growth in very different types of stress, including membrane stability, oxidative stress, and nitrogen limitation. Similarly, the eQTL hotspot is probably due to multiple causal genetic variants, as they impact genes with unrelated functions. In addition, most of the eQTL detected in the introgressed region affects the expression of genes located within this region and are probably due to variants in cis-regulatory elements. By considering only genes located outside this region, a weak enrichment in amino acid biosynthesis function (FunSpec P value: 1.5e−6) was found for the fitness QTL affecting growth on several nitrogen limited media. Additionally, as expected, we found an enrichment for genes involved in peptide pheromone maturation (FunSpec P value: 7.9e−5), explained by the presence of the mating-type regulator in the introgressed region.

Additionally, we observed another important consequence of MAT locus being present in the nonrecombinant region. All the growth traits and gene expression impacted by variants in this region are genetically linked to the mating type, which explains the results of the PCA (fig. 1C). It also means that these variants are hitchhiking the selection of the MAT alleles, necessary for mating. This adds up to a general consequence of absence of recombination: a possible accumulation of deleterious mutations in this region. This was confirmed using the average dN/dS values for each gene across the 28 sequenced L. kluyveri natural isolates (Friedrich et al. 2015). The dN/dS value corresponds to the ratio of density of nonsynonymous polymorphism reported to the density of synonymous polymorphism. This value is generally higher for the genes located in the introgressed region, including for genes highly conserved across species (supplementary fig. S8, Supplementary Material online) (Brion et al. 2015). This result indicates a lower negative selection on the protein sequences compared with the rest of the genome. The accumulation of these variants with potential impact on gene functions might explain the high number of fitness and expression QTL detected in this region.

High Density of Local Regulatory Variants Detected Is Related to High Genetic Diversity, Especially in the Introgressed Region

A high number of eQTL located on introgressed region are acting locally (local-eQTL), affecting expression of genes within this region. We wanted to investigate if the density of local-eQTL is higher in the introgression than in the rest of the genome. In our entire analysis, about 54% of the eQTL detected correspond to local-eQTL (1,188). As observed in other eQTL analysis, local-eQTL generally have a stronger effect than nonlocal-eQTL (999 distant-eQTL) (supplementary table S5 and fig. S7B, Supplementary Material online). Although local-eQTL can be due to a mutation that directly changes the gene expression in cis, for example by changing a binding site in the promoter or the stability of the mRNA, they can also be explained by mutation located in a nearby gene acting in trans, or mutation disrupting the function of the affected gene, thus changing its expression through a retro-control (Ronald et al. 2005).

These results revealed that the density of local-eQTL is much higher in the introgressed region (160 per Mb) compared with the rest of the genome (100 per Mb). We proposed that the high genetic diversity of the region is the main factor explaining this higher density, increasing the chance of variants affecting expression in cis. We confirmed this observation by comparing the density of local-eQTL across different crosses from multiple yeast species with various genetic diversities. We used eQTL data from a cross between RM11.1a and BY4741 S. cerevisiae strains, as well as between 968 and Y0036 Schizosaccharomyces pombe strains, with a genetic diversity of 0.5% and 0.05%, respectively (Clément-Ziza et al. 2014; Albert et al. 2018). Interestingly, we observed a strong correlation between the density of genetic variants and local-eQTL (fig. 3, Pearson R2: 0.98, P value: 0.001). This correlation allows us to explain the high density of local-eQTL in L. kluyveri and especially in the introgressed region. However, other factors are expected to impact the strong relationship observed here, such as the genome density and complexity of local regulation. Moreover, this correlation can be made only using data set with similar detection power, as an increased power allows to observe very small genetic effects. Consequently, local-eQTL have been detected for 50% of S. cerevisiae genes using a population of 1,012 segregants (Albert et al. 2018).

Fig. 3.

Fig. 3.

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Comparison of the relationship between the density of mutation and the density of local-eQTL across three yeast species. The gray line corresponds to a linear regression model (Pearson R2: 0.98, P value: 0.001). Only the 2,000 strongest eQTL were considered among the 36,498 eQTL detected in the Saccharomyces cerevisiae cross (see Materials and Methods). The data from the current study are indicated in shades of blue (Lachancea kluyveri) compared with other studies in green (S. cerevisiae, Albert et al. 2018) and red (S. pombe, Clément-Ziza et al. 2014).

Loci with Major Regulatory Effect Such as the One Detected on Chromosome E Appear to Be a Trend across Yeast Species

The major QTL hotspot located on the chromosome E, containing 23 fitness QTL and about half of the detected distant-eQTL (496), influences resistance to various environmental stresses. Consequently, we suspected that a part of the affected genes will be involved in the general environmental stress response (Gasch et al. 2000; Brion et al. 2016). Indeed, we found a significant enrichment of 101 environmental stress response genes among the 516 affected genes (Fisher’s exact test P value: 7.6e−4). The impacted genes are also functionally enriched for electron transport and membrane-associated energy conservation (FunSpec P value: <1e−14), tricarboxylic-acid pathway (FunSpec P value: 7.1e−7), transcription (FunSpec P value: 3.2e−6), and nucleus transport (FunSpec P value: 1.6e−4). However, based on the limited annotation of L. kluyveri, no clear genes or variants can be proposed as directly responsible for this QTL hotspot (supplementary fig. S9, Supplementary Material online).

The striking feature of this hotspot is the high number of gene expressions impacted by it (more than 500). However, similar hotspots have been found in other species such as in S. cerevisiae and S. pombe eQTL studies, where a major regulatory hotspot can be observed and narrowed down to the MKT1 (D30G variant affecting 460 genes) and swc5 (frameshift affecting 610 genes) genes, respectively. However, these genes are involved in nonoverlapping pathways (supplementary fig. S10, Supplementary Material online). MKT1 is involved in mRNA regulation and mitochondrial stability (Wickner 1987; Smith and Kruglyak 2008) and causes change of mitochondrial ribosomal protein and other mitochondrial proteins. By contrast in S. pombe, swc5 (component of the SWR1 complex) affects the expression of genes involved in cell cycle and chromosomal modification (Clément-Ziza et al. 2014).

Finally, although many genes are affected by the hotspot located on the chromosome E, it tends to have a marginal effect on their expression variance (on average 39% of the variance explained) and on the growth capacity in the environment used for RNA extraction (7.5% of the growth variance explained in rich medium). Similarly in S. cerevisiae, MKT1-D30G only explains 19% of the variance of growth on minimum medium, and on average 23% of the variance of gene expression (Albert et al. 2018).

Overall, major regulatory hotspots seem to be a trend conserved across yeast species. Although they are generally associated with a growth defect, the set of genes affected are specific to each case. Changing the environmental conditions likely creates a different major hotspot due to another genetic variant with a critical role catering to this new condition (Smith and Kruglyak 2008).

Discussion

Dissecting the genotype–phenotype relationship remains a major challenge. Substantial improvement and availability of whole genome sequencing of large populations allows a more systematic analysis of the genetic origins of phenotypic diversity. However, large genomic properties such as introgression events are still difficult to be exhaustively identified and consequently they are usually overlooked even if they are known to have an impact on the phenotypic landscape.

In this context, we sought to assess the phenotypic effects of a 1-Mb introgressed region present in a L. kluyveri population. Interestingly, this region has a significant impact on the phenotypic landscape as illustrated by the fact that it corresponds to a QTL hotspot, composed of 23 fitness QTL and 247 eQTL. In addition, the absence of recombination in this introgressed region that determines mating types genetically links all the genes and, consequently, all phenotypic characteristics their alleles might induce are linked to the MAT locus. As a direct result, MATa and MATα strains in the mapping population have very distinct phenotypic profiles and can perfectly be separated on a PCA. This observation clearly highlights the presence of a sexual dimorphism in L. kluyveri, that is, the association between the sex locus and some traits, which are initially unrelated to the determination of the sex.

In many plants and animals, the sex determination system is carried by chromosomes of different structure that does not recombine (X/Y or Z/W chromosomes). Among other fungi, only few cases of large recombination suppression (>100 kb) around the MAT locus/loci have been observed. In the filamentous fungi Neurospora tetrasperma, a 7-Mb nonrecombining region, with over 1,500 genes, containing the MAT locus has been linked to a progressive accumulation of inversions between the two mating types (Menkis et al. 2008; Samils et al. 2013; Idnurm et al. 2015). This region also contained the centromere of the chromosome and ensured segregation of the mating types at the first meiotic division. Similarly, in the genome of Podospora anserina, a region of 837 kb (229 genes) around the MAT locus is depleted of recombination. In this species however, the region from both mating types remained colinear, and other yet-to-defined factors are responsible of the recombination depletion (Grognet et al. 2014). In the Cryptococcus and Microbotryum spp., absence of recombination allows genetic linking of two regions carrying pheromone-receptor genes and homeodomain genes, conserving a 50% compatibility in the progeny (Hsueh et al. 2006; Fontanillas et al. 2015; Idnurm et al. 2015). Notably, in Microbotryum spp., the suppression of recombination happened progressively through rearrangements, which can be observed by the presence of strata of synonymous mutations in the genes located in the regions (Branco et al. 2017, 2018).

With the generation of a mapping population in L. kluyveri for QTL identification, we simultaneously discovered a large region unable to go through recombination and observed a striking consequence on a large panel of phenotypes, leading to sexual dimorphism. Those phenotypes are poorly related, involving resistance to environmental stress (e.g., H2O2 or CaCl2) or growth in nutrient limitation (nitrogen limited media). Our eQTL results also revealed that the expressions of many genes are affected by variants linked to the MAT locus and can be considered as MAT-controlled genes. However, the majority of them have no direct link with pheromone production, sensing, and mating. Although the key element leading to this dimorphism is the absence of recombination on this 1-Mb region, it is still unclear that what the molecular origin of it is. Neither can we clearly state that the presence of the MAT locus is the element leading to the recombination cold-spot or if these two characteristics are unrelated. In any case, this region has been acquired through an introgression event, possibly from the Eremothecium gossypii species (Landerer, O’Meara, Zaretzki, and Gilchrist, unpublished data), and our results represent an unforeseen consequences of such event in the dynamics of evolution of species.

Importantly, such genetic and phenotypic load in the MAT region with no recombination was similarly observed in the other fungus species with the same properties (Samils et al. 2013; Grognet et al. 2014; Fontanillas et al. 2015; Ma et al. 2020). In Neurospora tetrasperma and Microbotryum spp., genetic studies suggested that gene flow and introgression events occurred in such region as countermeasures to this genetic load (Corcoran et al. 2016; Hartmann et al. 2020). For two decades, it has been proposed that absence of recombination leads to genetic degeneration of the region around the MAT locus (Charlesworth B and Charlesworth D 2000). Indeed, all variants in these regions are genetically linked, preventing independent selection and elimination of deleterious mutations. The same phenomenon can be proposed in L. kluyveri where de novo mutations in the introgressed region would hijack the conservation of the MAT allele required for sexual cycle. This hypothesis can explain the high genetic diversity in the sequenced population and the higher dN/dS values for the genes located in this region (Brion et al. 2015; Friedrich et al. 2015). To confirm this hypothesis, we aim, in the future, to evaluate the rate of novel mutation along the genome in this species, through mutation accumulation experiments (Lynch et al. 2008).

Overall, by demonstrating that variations for an extensive set of phenotypes are linked to the MAT locus, we have shown the importance of given introgression features, that is, a higher diversity and recombination suppression, in shaping the phenotypic diversity of the species. In addition, we also highlighted, for the first time, the possibility of sexual dimorphism in a budding yeast.

Materials and Methods

Strains Construction and F1 Mapping Population

The construction of the mapping population was previously described in Brion et al. (2017). Briefly, the parental strains NBRC10955a MATa chs3Δ and 67-588 MATα chs3Δ were crossed on standard media (YPD: yeast extract 1%, peptone 2%, glucose 2%, and agar 2%) and a hybrid was isolated. The deletion of CHS3 allows for a better tetrad dissection (Sigwalt et al. 2016). The hybrid strain was put on sporulation media (potassium acetate 1% and agar 2%) for about a week. After digestion of the asci using zymolyase (0.5 mg/ml MP Biomedicals MT ImmunO 20T), 120 tetrads were dissected using a MSM 400 dissection microscope (Singer instrument). From the 120 tetrads dissected, 57 were completely viable. The 198 strains from 49 full-viable tetrads and two from a 50% viable tetrad were sequenced. The genomic DNA extracted using MasterPure Yeast DNA Purification Kit (tebu-bio: now Lucigen) was sequenced using Illumina HiSeq 2000 technology with 100-bp paired-end libraries. The reads were aligned to the CBS3082 reference genome using BWA (-n 8 -o 2 option) and the average coverage was around 70× for all the segregants. We used the same genetic markers as in our previous study. These 58,256 markers (single nucleotide polymorphisms) were selected to be highly reliable (see criteria in Brion et al. [2017]). An allelic origin was assigned for each marker position of each segregant using roughly the same criteria of reliability as for the genetic markers determination. Raw data are available on the European Nucleotide Archive (http://www.ebi.ac.uk/ena, last accessed April 30, 2020) under accession number PRJEB13706.

Growth Condition and Phenotyping

From the 198 sequenced segregants, 182 were allocated randomly on two 96-well plates, along with the parental strains, NBRC10955a and 67-588, and the hybrid NBRC10955a × 67-588. These two 96-well plates were used twice as matrix to create an YPD agar plate with 376 colonies (duplicates of the 182 segregant and four replicates for each parent on a 384 format). This plate was used as pregrowth for the fitness assessment. Cells for the colonies were transferred to two agar plates containing the test media using an automated pinning robot (Singer Instrument ROTOR HDA). This allows for a total of four replicates per strain and per condition. The list of the 64 growth conditions used is described in supplementary table S1, Supplementary Material online: two different temperatures, eight different carbon sources, 12 different unique nitrogen sources, 20 toxic compounds, including solvent, ions, or antifungal, at different concentrations, and four different pH. The pH was adjusted using a neutral citrate buffer. The base media was YP (yeast extract 1%, peptone 2%, and agar 2%), YNB (yeast nitrogen base without nitrogen 1.7 g l−1, glucose 2%, and agar 2%), or SC (yeast nitrogen base with nitrogen 6.7 g l−1, SC complement with all amino acid 2 g l−1, glucose 2%, and agar 2%). The plates were incubated at 30 °C, unless indicated otherwise in supplementary table S1, Supplementary Material online, for 40 h. An image of the scan of the plate was analyzed on R-gitter to obtain the area of each colony (data in supplementary table S2, Supplementary Material online). The quantitative fitness value was computed as the ratio between the tested media and the reference media, with the exception of YP glucose 2% where the raw colony size was used for quantitative value.

Generation of F2 Mapping Population

Using the genotyping data, we determined the mating type of the F1 segregant strains. We generated 69 crosses between MATa and MATα strains. The paired strains were chosen in order to keep an even ratio of both parental alleles. The set of hybrids were put on sporulation media (potassium acetate 1% and agar 2%) for 3–7 days at 30 °C. The asci of the tetrads were digested for 30 min using zymolyase (0.5 mg/ml MP Biomedicals MT ImmunO 20T) and about ten tetrads per cross were dissected using a MSM 400 dissection microscope (Singer Instrument). From 50 full viable tetrads, we selected 50 spores (one per tetrad) to constitute the F2 population. We controlled for the 50 F2 strains to be haploid using propidium iodine DNA staining and flow cytometry.

RNA Extraction and Sequencing

The transcriptome profiling was performed during exponential growth in rich media. The 50 F2 segregants and the parental strains were pregrown in YPD overnight. A flask of 30 ml of YPD was inoculating at a final optical density (OD600 λ = 600 nm) of 0.05. The OD600 was monitored and, when it reached 0.350–0.450, 7 ml of the culture was filtered, washed with sterile water, and immediately frozen in liquid nitrogen and store at −80 °C.

For RNA extraction, the cells were lysed in lysis buffer (ethylenediaminetetraacetic acid 10 mM, SDS 0.5%, TricHCl 10 mM, pH 7.5) and aqua-phenol (MP AQUAPHEO01) at 65 °C for 1 h. The aqueous phase was purified from protein trace by a centrifugation in PLG tube (5prime 2302830). After precipitation in 70% ethanol overnight, the RNA was purified using the RNeasy kit (Qiagen 74104, Venlo, Limburg, the Netherlands) and cleaned with DNase treatment (Cat No. 18068-015, Invitrogen, Carlsbad, CA). The quality was controlled by a migration on agarose gel, and RNA was quantified using spectrophotometry (NanoDrop ND-1000).

The sequencing of the 52 RNA samples was performed at the Gene Core Facility - EMBL (Heidelberg, Germany) using multiplexed Illumina HiSeq2000 sequencing (50-bp nonoriented single end reads). Raw reads data are available on the European Nucleotide Archive (http://www.ebi.ac.uk/ena, last accessed April 30, 2020) under accession number PRJEB32833. The normalization was performed as described in Brion et al. (2015). Briefly, after excluding low expressed genes (number of reads lower than 32 for all samples), intersample normalization was performed using R and DEseq2 (R Core Team 2013; Love et al. 2014). Gene expressions from the retrogressed region were normalized separately due to the bias caused by the higher GC content. The base 2 logarithm of the normalized reads count was used as quantitative value for the abundance of mRNA.

Genotyping of the F2 Population

Using RNA-seq data, the F2 population was genotyped by determining the allelic origin of 58,256 markers previously used for the F1 population. We mapped the reads from RNA-seq on the reference genome and identified variants using SAMtools (Li et al. 2009). An allelic origin was inferred at a marker position if 1) the coverage of the marker was more than 2×, 2) the associated allele frequency was 0 or 1, and 3) the base sequenced fit one of the two parental alleles. If not, a NA flag was assigned to the position. The marker was kept for the next step if 1) it was located in a coding region, 2) had <30% of the F2 population that display a NA flag on its position, and 3) showed an unbalanced allele frequency in the F2 population (more than 0.7 or <0.3). Finally, post filtration, a total of 37,529 markers were retained. In order to reduce the computation time, we artificially reduced the number of markers to 3,779 using pseudomarker every 3 kb. The allele origin assigned to the pseudomarkers was the predominant allele origin of all the markers within 4 kb of the pseudomarker. Given the low recombination rate of L. kluyveri, this approximation did not generate a strong loss of accuracy.

Fitness and Expression Heritability

The broad-sense heritability, H2, is defined as the part of the variance explained by heritable factors. In our case, it has been defined as the variance in the segregating population that is not explained by the experimental noise. For fitness data, the variance explained by the experimental noise was estimated using the mean of the variance across the parental replicates (four replicates for the two parental strains and the hybrid strain). For the expression data, we did not have replicates to estimate the noise, therefore we used the variance of expression across replicates from another data set of expression across 24 L. kluyveri strains published in Brion et al. (2015). This data set corresponds to expression profiling in the same condition (mid-log phase, in liquid YPD) and follows exactly the same protocol for RNA extraction and sequencing.

Fitness QTL and eQTL Analysis

The linkage analysis between quantitative phenotype (growth ratio and mRNA abundance) and genotype was performed using R/qtl (model: normal, method: Haley-Knott regression) (Arends et al. 2010). The phenotypic QTL were obtained using 182 F1 segregants. The significant thresholds of LOD score (score of the logarithm of the odds) were defined individually for each phenotype performing 1,000 permutations and setting the LOD score for which QTL were detected in only 50 permutations (FDR = 5%).

The expression QTL were also obtained using R/qtl (model: normal, method: Haley-Knott regression) based on the normalized read count of the 50 F2 segregants and the two parental strains. To define the FDR, we performed 100 permutations of the complete data set and processed the eQTL analysis. For a range of LOD score threshold, the FDR was estimated as the average number of detected false-eQTL across the 100 permutations divided by the number of eQTL detected in the true data set (supplementary fig. S5, Supplementary Material online). We used an overall significant LOD score threshold of 3.7 which allowed to detect 2,187 eQTL with an FDR of ∼4%. The use of a more stringent LOD score threshold of 4.4 would have led to a detection of 1,638 eQTL with an FDR of ∼1%. Any functional enrichment was tested using the online tool FunSpec (Robinson et al. 2002).

Expression QTL from S. cerevisiae and S. pombe Used for Interspecies Comparison

To compare the genetic control of gene expression across species, we used published data from eQTL analysis in S. cerevisiae and S. pombe. For S. cerevisiae eQTL data, we used the 36,498 eQTL obtained from the population of 1,012 segregants for the RM11.1a and BY4741 cross (0.5% diversity) (Albert et al. 2018). However, because the detection power is much higher due to the large population, we only focus on the best 2,000 eQTL (2,000 highest LOD score, LOD score > 39.3), estimating that a population of about 50 segregants would have allowed for the identification of these eQTL. For S. pombe, we used the 2,346 eQTL obtained from a cross between 968 and Y0036 (0.05% diversity) with 44 F2 segregants (Clément-Ziza et al. 2014).

Supplementary Material

Supplementary data are available at Molecular Biology and Evolution online.

Supplementary Material

msaa106_Supplementary_Data
Click here for additional data file. (729.3KB, pdf)

Acknowledgments

We are grateful to Gargi Dayama for insightful comments on the manuscript. We thank the BioImage platform (IBMP-CNRS, Strasbourg, France) for their support. This work was supported by the Agence Nationale de la Recherche (ANR-18-CE12-0013-02) and a European Research Council (ERC) Consolidator grant (772505). J.S. is a fellow of the University of Strasbourg Institute for Advanced Study (USIAS) and a member of the Institut Universitaire de France.

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Supplementary Materials

msaa106_Supplementary_Data
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