Advances in quantitative trait analysis in yeast

Genetic basis of variations in nitrogen source utilization in four wine commercial yeast strains

The capacity of wine yeast to utilize the nitrogen available in grape must directly correlates with the fermentation and growth rates of all wine yeast fermentation stages and is, thus, of critical importance for wine production. Here we precisely quantified the ability of low complexity nitrogen compounds to support fast, efficient and rapidly initiated growth of four commercially important wine strains. Nitrogen substrate abundance in grape must failed to correlate with the rate or the efficiency of nitrogen source utilization, but well predicted lag phase length. Thus, human domestication of yeast for grape must growth has had, at the most, a marginal impact on wine yeast growth rates and efficiencies, but may have left a surprising imprint on the time required to adjust metabolism from non growth to growth. Wine yeast nitrogen source utilization deviated from that of the lab strain experimentation, but also varied between wine strains. Each wine yeast lineage harbored nitrogen source utilization defects that were private to that strain. By a massive hemizygote analysis, we traced the genetic basis of the most glaring of these defects, near inability of the PDM wine strain to utilize methionine, as consequence of mutations in its ARO8, ADE5,7 and VBA3 alleles. We also identified candidate causative mutations in these genes. The methionine defect of PDM is potentially very interesting as the strain can, in some circumstances, overproduce foul tasting H2S, a trait which likely stems from insufficient methionine catabolization. The poor adaptation of wine yeast to the grape must nitrogen environment, and the presence of defects in each lineage, open up wine strain optimization through biotechnological endeavors.

Theoretical model for gene-gene, gene-environment, and gene-sex interactions based on congenic-strain analysis of blood pressure in Dahl salt-sensitive rats

There is a significant literature describing quantitative trait loci (QTL) controlling blood pressure (BP) in the Dahl salt-sensitive (S) rat. In studies to identify the genes underlying BP QTL it has been common practice to place chromosomal segments from low BP strains on the genetic background of the S rat and then reduce the congenic segments by substitution mapping. The present work suggests a model to simulate genetic interactions found using such congenic strains. The QTL are considered to be switches that can be either in series or in parallel represented by the logic operators AND or OR, respectively. The QTL switches can be on/off switches but are also allowed specific leak properties. The QTL switches are represented by a "universal" switch consisting of two molecules binding to form a complex. Genetic inputs enter the model as allelic products of one of the binding molecules and environmental variation (including dietary salt- and sex-related differences) enters as an influence on the concentration of the other binding molecule. The pairwise interactions of QTL are very well simulated and fall into recognizable patterns. There is, however, often more than one assumed model to predict a given pattern so that all patterns do not necessarily have a unique solution. Nevertheless, the models obtained provide a framework for placing the QTL in pathways relative to one another. Moreover, based on their leak properties pairs of QTL could be identified in which one QTL may alter the properties of the other QTL.

Identification of multiple interacting alleles conferring low glycerol and high ethanol yield in Saccharomyces cerevisiae ethanolic fermentation

BACKGROUND

Genetic engineering of industrial microorganisms often suffers from undesirable side effects on essential functions. Reverse engineering is an alternative strategy to improve multifactorial traits like low glycerol/high ethanol yield in yeast fermentation. Previous rational engineering of this trait always affected essential functions like growth and stress tolerance. We have screened Saccharomyces cerevisiae biodiversity for specific alleles causing lower glycerol/higher ethanol yield, assuming higher compatibility with normal cellular functionality. Previous work identified ssk1E330N…K356N as causative allele in strain CBS6412, which displayed the lowest glycerol/ethanol ratio.

RESULTS

We have now identified a unique segregant, 26B, that shows similar low glycerol/high ethanol production as the superior parent, but lacks the ssk1E330N…K356N allele. Using segregants from the backcross of 26B with the inferior parent strain, we applied pooled-segregant whole-genome sequence analysis and identified three minor quantitative trait loci (QTLs) linked to low glycerol/high ethanol production. Within these QTLs, we identified three novel alleles of known regulatory and structural genes of glycerol metabolism, smp1R110Q,P269Q, hot1P107S,H274Y and gpd1L164P as causative genes. All three genes separately caused a significant drop in the glycerol/ethanol production ratio, while gpd1L164P appeared to be epistatically suppressed by other alleles in the superior parent. The order of potency in reducing the glycerol/ethanol ratio of the three alleles was: gpd1L164P > hot1P107S,H274Y ≥ smp1R110Q,P269Q.

CONCLUSIONS

Our results show that natural yeast strains harbor multiple specific alleles of genes controlling essential functions, that are apparently compatible with survival in the natural environment. These newly identified alleles can be used as gene tools for engineering industrial yeast strains with multiple subtle changes, minimizing the risk of negatively affecting other essential functions. The gene tools act at the transcriptional, regulatory or structural gene level, distributing the impact over multiple targets and thus further minimizing possible side-effects. In addition, the results suggest polygenic analysis of complex traits as a promising new avenue to identify novel components involved in cellular functions, including those important in industrial applications.

Comparative polygenic analysis of maximal ethanol accumulation capacity and tolerance to high ethanol levels of cell proliferation in yeast

The yeast Saccharomyces cerevisiae is able to accumulate ≥17% ethanol (v/v) by fermentation in the absence of cell proliferation. The genetic basis of this unique capacity is unknown. Up to now, all research has focused on tolerance of yeast cell proliferation to high ethanol levels. Comparison of maximal ethanol accumulation capacity and ethanol tolerance of cell proliferation in 68 yeast strains showed a poor correlation, but higher ethanol tolerance of cell proliferation clearly increased the likelihood of superior maximal ethanol accumulation capacity. We have applied pooled-segregant whole-genome sequence analysis to identify the polygenic basis of these two complex traits using segregants from a cross of a haploid derivative of the sake strain CBS1585 and the lab strain BY. From a total of 301 segregants, 22 superior segregants accumulating ≥17% ethanol in small-scale fermentations and 32 superior segregants growing in the presence of 18% ethanol, were separately pooled and sequenced. Plotting SNP variant frequency against chromosomal position revealed eleven and eight Quantitative Trait Loci (QTLs) for the two traits, respectively, and showed that the genetic basis of the two traits is partially different. Fine-mapping and Reciprocal Hemizygosity Analysis identified ADE1, URA3, and KIN3, encoding a protein kinase involved in DNA damage repair, as specific causative genes for maximal ethanol accumulation capacity. These genes, as well as the previously identified MKT1 gene, were not linked in this genetic background to tolerance of cell proliferation to high ethanol levels. The superior KIN3 allele contained two SNPs, which are absent in all yeast strains sequenced up to now. This work provides the first insight in the genetic basis of maximal ethanol accumulation capacity in yeast and reveals for the first time the importance of DNA damage repair in yeast ethanol tolerance.

The yeast Saccharomyces cerevisiae is unique in being the most ethanol tolerant organism known. This property lies at the basis of its ecological competitiveness in sugar-rich ecological niches and its use for the production of alcoholic beverages and bioethanol, both of which involve accumulation of high levels of ethanol. Up to now, all research on yeast ethanol tolerance has focused on tolerance of cell proliferation to high ethanol levels. However, the most ecologically and industrially relevant aspect is the capacity of fermenting yeast cells to accumulate high ethanol levels in the absence of cell proliferation. Using QTL mapping by pooled-segregant whole-genome sequence analysis, we show that maximal ethanol accumulation capacity and tolerance of cell proliferation to high ethanol levels have a partially different genetic basis. We identified three specific genes responsible for high ethanol accumulation capacity, of which one gene encodes a protein kinase involved in DNA damage repair. Our work provides the first insight in the genetic basis of maximal ethanol accumulation capacity, shows that it involves different genetic elements compared to tolerance of cell proliferation to high ethanol levels, and reveals for the first time the importance of DNA damage repair in ethanol tolerance.

Inferring genome-wide recombination landscapes from advanced intercross lines: application to yeast crosses

Accurate estimates of recombination rates are of great importance for understanding evolution. In an experimental genetic cross, recombination breaks apart and rejoins genetic material, such that the genomes of the resulting isolates are comprised of distinct blocks of differing parental origin. We here describe a method exploiting this fact to infer genome-wide recombination profiles from sequenced isolates from an advanced intercross line (AIL). We verified the accuracy of the method against simulated data. Next, we sequenced 192 isolates from a twelve-generation cross between West African and North American yeast Saccharomyces cerevisiae strains and inferred the underlying recombination landscape at a fine genomic resolution (mean segregating site distance 0.22 kb). Comparison was made with landscapes inferred for a similar cross between four yeast strains, and with a previous single-generation, intra-strain cross (Mancera et al., Nature 2008). Moderate congruence was identified between landscapes (correlation 0.58-0.77 at 5 kb resolution), albeit with variance between mean genome-wide recombination rates. The multiple generations of mating undergone in the AILs gave more precise inference of recombination rates than could be achieved from a single-generation cross, in particular in identifying recombination cold-spots. The recombination landscapes we describe have particular utility; both AILs are part of a resource to study complex yeast traits (see e.g. Parts et al., Genome Res 2011). Our results will enable future applications of this resource to take better account of local linkage structure heterogeneities. Our method has general applicability to other crossing experiments, including a variety of experimental designs.

Saccharomyces diversity and evolution: a budding model genus

Saccharomyces cerevisiae is one of the best-understood and most powerful genetic model systems. Several disciplines are now converging to turn Saccharomyces into an exciting model genus for evolutionary genetics and genomics. Yeast taxonomists and ecologists have dramatically expanded and clarified Saccharomyces diversity, more than doubling the number of bona fide species since 2000. High-quality genome sequences are available (or soon will be) for all seven known species. Haploid laboratory strains are enabling a deep integration of classic genetic approaches with modern genomic tools. Population genomic surveys and quantitative trait mapping of variation within species are underway across the genus. Finally, several case studies have illuminated general and novel genetic mechanisms of evolution. Expanding strain collections, low-cost genome sequencing, and tools for precise genetic manipulation promise to usher in a golden era for this surprisingly diverse genus as an evolutionary model.

Genotype to phenotype: lessons from model organisms for human genetics

To what extent can variation in phenotypic traits such as disease risk be accurately predicted in individuals? In this Review, I highlight recent studies in model organisms that are relevant both to the challenge of accurately predicting phenotypic variation from individual genome sequences ('whole-genome reverse genetics') and for understanding why, in many cases, this may be impossible. These studies argue that only by combining genetic knowledge with in vivo measurements of biological states will it be possible to make accurate genetic predictions for individual humans.

The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae is the main microorganism responsible for wine alcoholic fermentation. The oenological phenotypes resulting from fermentation, such as the production of acetic acid, glycerol, and residual sugar concentration are regulated by multiple genes and vary quantitatively between different strain backgrounds. With the aim of identifying the quantitative trait loci (QTLs) that regulate oenological phenotypes, we performed linkage analysis using three crosses between highly diverged S. cerevisiae strains. Segregants from each cross were used as starter cultures for 20-day fermentations, in synthetic wine must, to simulate actual winemaking conditions. Linkage analysis on phenotypes of primary industrial importance resulted in the mapping of 18 QTLs. We tested 18 candidate genes, by reciprocal hemizygosity, for their contribution to the observed phenotypic variation, and validated five genes and the chromosome II right subtelomeric region. We observed that genes involved in mitochondrial metabolism, sugar transport, nitrogen metabolism, and the uncharacterized ORF YJR030W explained most of the phenotypic variation in oenological traits. Furthermore, we experimentally validated an exceptionally strong epistatic interaction resulting in high level of succinic acid between the Sake FLX1 allele and the Wine/European MDH2 allele. Overall, our work demonstrates the complex genetic basis underlying wine traits, including natural allelic variation, antagonistic linked QTLs and complex epistatic interactions between alleles from strains with different evolutionary histories.