Identification of novel causative genes determining the complex trait of high ethanol tolerance in yeast using pooled-segregant whole-genome sequence analysis

Biofuels. Engineering alcohol tolerance in yeast

Ethanol toxicity in the yeast Saccharomyces cerevisiae limits titer and productivity in the industrial production of transportation bioethanol. We show that strengthening the opposing potassium and proton electrochemical membrane gradients is a mechanism that enhances general resistance to multiple alcohols. The elevation of extracellular potassium and pH physically bolsters these gradients, increasing tolerance to higher alcohols and ethanol fermentation in commercial and laboratory strains (including a xylose-fermenting strain) under industrial-like conditions. Production per cell remains largely unchanged, with improvements deriving from heightened population viability. Likewise, up-regulation of the potassium and proton pumps in the laboratory strain enhances performance to levels exceeding those of industrial strains. Although genetically complex, alcohol tolerance can thus be dominated by a single cellular process, one controlled by a major physicochemical component but amenable to biological augmentation.

Identification of loci that cause phenotypic variation in diverse species with the reciprocal hemizygosity test

The reciprocal hemizygosity test is a straightforward genetic test that can positively identify genes that have evolved to contribute to a phenotypic difference between strains or between species. The test involves a comparison between hybrids that are genetically identical throughout the genome except at the test locus, which is rendered hemizygous for alternative alleles from the two parental strains. If the two reciprocal hemizygotes display different phenotypes, then the two parental alleles must have evolved. New methods for targeted mutagenesis will allow application of the reciprocal hemizygosity test in many organisms. This review discusses the principles, advantages, and limitations of the test.

Phenotypic evaluation of natural and industrial Saccharomyces yeasts for different traits desirable in industrial bioethanol production

Saccharomyces cerevisiae is the organism of choice for many food and beverage fermentations because it thrives in high-sugar and high-ethanol conditions. However, the conditions encountered in bioethanol fermentation pose specific challenges, including extremely high sugar and ethanol concentrations, high temperature, and the presence of specific toxic compounds. It is generally considered that exploring the natural biodiversity of Saccharomyces strains may be an interesting route to find superior bioethanol strains and may also improve our understanding of the challenges faced by yeast cells during bioethanol fermentation. In this study, we phenotypically evaluated a large collection of diverse Saccharomyces strains on six selective traits relevant for bioethanol production with increasing stress intensity. Our results demonstrate a remarkably large phenotypic diversity among different Saccharomyces species and among S. cerevisiae strains from different origins. Currently applied bioethanol strains showed a high tolerance to many of these relevant traits, but several other natural and industrial S. cerevisiae strains outcompeted the bioethanol strains for specific traits. These multitolerant strains performed well in fermentation experiments mimicking industrial bioethanol production. Together, our results illustrate the potential of phenotyping the natural biodiversity of yeasts to find superior industrial strains that may be used in bioethanol production or can be used as a basis for further strain improvement through genetic engineering, experimental evolution, or breeding. Additionally, our study provides a basis for new insights into the relationships between tolerance to different stressors.

Using next-generation sequencing to isolate mutant genes from forward genetic screens

The long-lasting success of forward genetic screens relies on the simple molecular basis of the characterized phenotypes, which are typically caused by mutations in single genes. Mapping the location of causal mutations using genetic crosses has traditionally been a complex, multistep procedure, but next-generation sequencing now allows the rapid identification of causal mutations at single-nucleotide resolution even in complex genetic backgrounds. Recent advances of this mapping-by-sequencing approach include methods that are independent of reference genome sequences, genetic crosses and any kind of linkage information, which make forward genetics amenable for species that have not been considered for forward genetic screens so far.

The genetic basis of variation in clean lineages of Saccharomyces cerevisiae in response to stresses encountered during bioethanol fermentations

Saccharomyces cerevisiae is the micro-organism of choice for the conversion of monomeric sugars into bioethanol. Industrial bioethanol fermentations are intrinsically stressful environments for yeast and the adaptive protective response varies between strain backgrounds. With the aim of identifying quantitative trait loci (QTL's) that regulate phenotypic variation, linkage analysis on six F1 crosses from four highly divergent clean lineages of S. cerevisiae was performed. Segregants from each cross were assessed for tolerance to a range of stresses encountered during industrial bioethanol fermentations. Tolerance levels within populations of F1 segregants to stress conditions differed and displayed transgressive variation. Linkage analysis resulted in the identification of QTL's for tolerance to weak acid and osmotic stress. We tested candidate genes within loci identified by QTL using reciprocal hemizygosity analysis to ascertain their contribution to the observed phenotypic variation; this approach validated a gene (COX20) for weak acid stress and a gene (RCK2) for osmotic stress. Hemizygous transformants with a sensitive phenotype carried a COX20 allele from a weak acid sensitive parent with an alteration in its protein coding compared with other S. cerevisiae strains. RCK2 alleles reveal peptide differences between parental strains and the importance of these changes is currently being ascertained.

Re-assessment of YAP1 and MCR1 contributions to inhibitor tolerance in robust engineered Saccharomyces cerevisiae fermenting undetoxified lignocellulosic hydrolysate

Development of robust yeast strains that can efficiently ferment lignocellulose-based feedstocks is one of the requirements for achieving economically feasible bioethanol production processes. With this goal, several genes have been identified as promising candidates to confer improved tolerance to S. cerevisiae. In most of the cases, however, the evaluation of the genetic modification was performed only in laboratory strains, that is, in strains that are known to be quite sensitive to various types of stresses. In the present study, we evaluated the effects of overexpressing genes encoding the transcription factor (YAP1) and the mitochondrial NADH-cytochrome b5 reductase (MCR1), either alone or in combination, in an already robust and xylose-consuming industrial strain of S. cerevisiae and evaluated the effect during the fermentation of undiluted and undetoxified spruce hydrolysate. Overexpression of either gene resulted in faster hexose catabolism, but no cumulative effect was observed with the simultaneous overexpression. The improved phenotype of MCR1 overexpression appeared to be related, at least in part, to a faster furaldehyde reduction capacity, indicating that this reductase may have a wider substrate range than previously reported. Unexpectedly a decreased xylose fermentation rate was also observed in YAP1 overexpressing strains and possible reasons behind this phenotype are discussed.

QTL-seq identifies an early flowering QTL located near Flowering Locus T in cucumber

Next-generation sequencing enabled a fast discovery of a major QTL controlling early flowering in cucumber, corresponding to the FT gene conditioning flowering time in Arabidopsis. Next-generation sequencing technologies are making it faster and more efficient to establish the association of agronomic traits with molecular markers or candidate genes, which is the requirement for marker-assisted selection in molecular breeding. Early flowering is an important agronomic trait in cucumber (Cucumis sativus L.), but the underlying genetic mechanism is unknown. In this study, we identified a candidate gene for early flowering QTL, Ef1.1 through QTL-seq. Segregation analysis in F2 and BC1 populations derived from a cross between two inbred lines "Muromskij" (early flowering) and "9930" (late flowering) suggested quantitative nature of flowering time in cucumber. Genome-wide comparison of SNP profiles between the early and late-flowering bulks constructed from F2 plants identified a major QTL, designated Ef1.1 on cucumber chromosome 1 for early flowering in Muromskij, which was confirmed by microsatellite marker-based classical QTL mapping in the F2 population. Joint QTL-seq and traditional QTL analysis delimited Ef1.1 to an 890 kb genomic region. A cucumber gene, Csa1G651710, was identified in this region, which is a homolog of the FLOWERING LOCUS T (FT), the main flowering switch gene in Arabidopsis. Quantitative RT-PCR study of the expression level of Csa1G651710 revealed significantly higher expression in early flowering genotypes. Data presented here provide support for Csa1G651710 as a possible candidate gene for early flowering in the cucumber line Muromskij.

Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on "hotspot" loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as "epi-hotspots," in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Mapping small effect mutations in Saccharomyces cerevisiae: impacts of experimental design and mutational properties

Genetic variants identified by mapping are biased toward large phenotypic effects because of methodologic challenges for detecting genetic variants with small phenotypic effects. Recently, bulk segregant analysis combined with next-generation sequencing (BSA-seq) was shown to be a powerful and cost-effective way to map small effect variants in natural populations. Here, we examine the power of BSA-seq for efficiently mapping small effect mutations isolated from a mutagenesis screen. Specifically, we determined the impact of segregant population size, intensity of phenotypic selection to collect segregants, number of mitotic generations between meiosis and sequencing, and average sequencing depth on power for mapping mutations with a range of effects on the phenotypic mean and standard deviation as well as relative fitness. We then used BSA-seq to map the mutations responsible for three ethyl methanesulfonate−induced mutant phenotypes in Saccharomyces cerevisiae. These mutants display small quantitative variation in the mean expression of a fluorescent reporter gene (−3%, +7%, and +10%). Using a genetic background with increased meiosis rate, a reliable mating type marker, and fluorescence-activated cell sorting to efficiently score large segregating populations and isolate cells with extreme phenotypes, we successfully mapped and functionally confirmed a single point mutation responsible for the mutant phenotype in all three cases. Our simulations and experimental data show that the effects of a causative site not only on the mean phenotype, but also on its standard deviation and relative fitness should be considered when mapping genetic variants in microorganisms such as yeast that require population growth steps for BSA-seq.

Improved linkage analysis of Quantitative Trait Loci using bulk segregants unveils a novel determinant of high ethanol tolerance in yeast

Background

Bulk segregant analysis (BSA) coupled to high throughput sequencing is a powerful method to map genomic regions related with phenotypes of interest. It relies on crossing two parents, one inferior and one superior for a trait of interest. Segregants displaying the trait of the superior parent are pooled, the DNA extracted and sequenced. Genomic regions linked to the trait of interest are identified by searching the pool for overrepresented alleles that normally originate from the superior parent. BSA data analysis is non-trivial due to sequencing, alignment and screening errors.

Results

To increase the power of the BSA technology and obtain a better distinction between spuriously and truly linked regions, we developed EXPLoRA (EXtraction of over-rePresented aLleles in BSA), an algorithm for BSA data analysis that explicitly models the dependency between neighboring marker sites by exploiting the properties of linkage disequilibrium through a Hidden Markov Model (HMM).

Reanalyzing a BSA dataset for high ethanol tolerance in yeast allowed reliably identifying QTLs linked to this phenotype that could not be identified with statistical significance in the original study. Experimental validation of one of the least pronounced linked regions, by identifying its causative gene VPS70, confirmed the potential of our method.

Conclusions

EXPLoRA has a performance at least as good as the state-of-the-art and it is robust even at low signal to noise ratio’s i.e. when the true linkage signal is diluted by sampling, screening errors or when few segregants are available.

Trypanosome MKT1 and the RNA-binding protein ZC3H11: interactions and potential roles in post-transcriptional regulatory networks

The trypanosome zinc finger protein ZC3H11 binds to AU-rich elements in mRNAs. It is essential for survival of the mammalian-infective bloodstream form, where it stabilizes several mRNAs including some encoding chaperones, and is also required for stabilization of chaperone mRNAs during the heat-shock response in the vector-infective procyclic form. When ZC3H11 was artificially 'tethered' to a reporter mRNA in bloodstream forms it increased reporter expression. We here show that ZC3H11 interacts with trypanosome MKT1 and PBP1, and that domains required for both interactions are necessary for function in the bloodstream-form tethering assay. PBP1 interacts with MKT1, LSM12 and poly(A) binding protein, and localizes to granules during parasite starvation. All of these proteins are essential for bloodstream-form trypanosome survival and increase gene expression in the tethering assay. MKT1 is cytosolic and polysome associated. Using a yeast two-hybrid screen and tandem affinity purification we found that trypanosome MKT1 interacts with multiple RNA-binding proteins and other potential RNA regulators, placing it at the centre of a post-transcriptional regulatory network. A consensus interaction sequence, H(E/D/N/Q)PY, was identified. Recruitment of MKT1-containing regulatory complexes to mRNAs via sequence-specific mRNA-binding proteins could thus control several different post-transcriptional regulons.

QTL mapping by pooled-segregant whole-genome sequencing in yeast

Quantitative trait locus (QTL) mapping by pooled-segregant whole-genome sequencing in yeast is a robust methodology for the simultaneous identification of superior genes involved in polygenic traits (e.g., high ethanol tolerance). By crossing two haploid strains with opposite phenotypes, being one of interest, the resulting diploid is sporulated, the meiotic segregants phenotyped, and a pool of selected segregants with the phenotype of interest assembled. The genotyping by pooled-segregant sequencing constitutes a fast and reliable methodology to map all QTL defining the trait of interest. The QTLs can be further analyzed by reciprocal hemizygosity analysis to identify the causative superior alleles that can subsequently be used for yeast strain improvement by targeted genetic engineering.

An evaluation of high-throughput approaches to QTL mapping in Saccharomyces cerevisiae

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals or of polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying 11 phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely (1) individual analysis of >700 meiotic segregants, (2) bulk segregant analysis, and (3) reciprocal hemizygosity scanning, a new genome-wide method that we developed. We reveal differences in the performance of each approach and, by combining them, identify eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on two nonfermentable carbon sources, and resistance to two drugs, salt, and high temperature. Our results demonstrate the power of individual segregant analysis to dissect QTL and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies.

The molecular basis of phenotypic variation in yeast

The power of yeast genetics has now been extensively applied to phenotypic variation among strains of Saccharomyces cerevisiae. As a result, over 100 genes and numerous sequence variants have been identified, providing us with a general characterization of mutations underlying quantitative trait variation. Most quantitative trait alleles exert considerable phenotypic effects and alter conserved amino acid positions within protein coding sequences. When examined, quantitative trait alleles influence the expression of numerous genes, most of which are unrelated to an allele's phenotypic effect. The profile of quantitative trait alleles has proven useful to reverse quantitative genetics approaches and supports the use of systems genetics approaches to synthesize the molecular basis of trait variation across multiple strains.

Re-evaluation of glycerol utilization in Saccharomyces cerevisiae: characterization of an isolate that grows on glycerol without supporting supplements

BACKGROUND

Glycerol has attracted attention as a carbon source for microbial production processes due to the large amounts of crude glycerol waste resulting from biodiesel production. The current knowledge about the genetics and physiology of glycerol uptake and catabolism in the versatile industrial biotechnology production host Saccharomyces cerevisiae has been mainly based on auxotrophic laboratory strains, and carried out in the presence of growth-supporting supplements such as amino acids and nucleic bases. The latter may have resulted in ambiguous conclusions concerning glycerol growth in this species. The purpose of this study was to re-evaluate growth of S. cerevisiae in synthetic glycerol medium without the addition of supplements.

RESULTS

Initial experiments showed that prototrophic versions of the laboratory strains CEN.PK, W303, and S288c did not exhibit any growth in synthetic glycerol medium without supporting supplements. However, a screening of 52 S. cerevisiae isolates for growth in the same medium revealed a high intraspecies diversity. Within this group significant variation with respect to the lag phase and maximum specific growth rate was observed. A haploid segregant of one good glycerol grower (CBS 6412-13A) was selected for detailed analysis. Single deletions of the genes encoding for the glycerol/H+ symporter (STL1), the glycerol kinase (GUT1), and the mitochondrial FAD+-dependent glycerol 3-phosphate dehydrogenase (GUT2) abolished glycerol growth in this strain, implying that it uses the same glycerol utilization pathway as previously identified in auxotrophic laboratory strains. Segregant analysis of a cross between CBS 6412-13A and CEN.PK113-1A revealed that the glycerol growth phenotype is a quantitative trait. Genetic linkage and reciprocal hemizygosity analysis demonstrated that GUT1CBS 6412-13A is one of the multiple genetic loci contributing to the glycerol growth phenotype.

CONCLUSION

The S. cerevisiae intraspecies diversity with regard to glycerol growth is a valuable starting point to identify the genetic and molecular basis of this phenotype. This knowledge can be applied for further rational strain improvement with the goal of using glycerol as a carbon source in industrial biotechnology processes based on S. cerevisiae as a production organism.

Genetically based location from triploid populations and gene ontology of a 3.3-mb genome region linked to Alternaria brown spot resistance in citrus reveal clusters of resistance genes

Genetic analysis of phenotypical traits and marker-trait association in polyploid species is generally considered as a challenge. In the present work, different approaches were combined taking advantage of the particular genetic structures of 2n gametes resulting from second division restitution (SDR) to map a genome region linked to Alternaria brown spot (ABS) resistance in triploid citrus progeny. ABS in citrus is a serious disease caused by the tangerine pathotype of the fungus Alternaria alternata. This pathogen produces ACT-toxin, which induces necrotic lesions on fruit and young leaves, defoliation and fruit drop in susceptible genotypes. It is a strong concern for triploid breeding programs aiming to produce seedless mandarin cultivars. The monolocus dominant inheritance of susceptibility, proposed on the basis of diploid population studies, was corroborated in triploid progeny. Bulk segregant analysis coupled with genome scan using a large set of genetically mapped SNP markers and targeted genetic mapping by half tetrad analysis, using SSR and SNP markers, allowed locating a 3.3 Mb genomic region linked to ABS resistance near the centromere of chromosome III. Clusters of resistance genes were identified by gene ontology analysis of this genomic region. Some of these genes are good candidates to control the dominant susceptibility to the ACT-toxin. SSR and SNP markers were developed for efficient early marker-assisted selection of ABS resistant hybrids.

QTL analysis of high thermotolerance with superior and downgraded parental yeast strains reveals new minor QTLs and converges on novel causative alleles involved in RNA processing

Revealing QTLs with a minor effect in complex traits remains difficult. Initial strategies had limited success because of interference by major QTLs and epistasis. New strategies focused on eliminating major QTLs in subsequent mapping experiments. Since genetic analysis of superior segregants from natural diploid strains usually also reveals QTLs linked to the inferior parent, we have extended this strategy for minor QTL identification by eliminating QTLs in both parent strains and repeating the QTL mapping with pooled-segregant whole-genome sequence analysis. We first mapped multiple QTLs responsible for high thermotolerance in a natural yeast strain, MUCL28177, compared to the laboratory strain, BY4742. Using single and bulk reciprocal hemizygosity analysis we identified MKT1 and PRP42 as causative genes in QTLs linked to the superior and inferior parent, respectively. We subsequently downgraded both parents by replacing their superior allele with the inferior allele of the other parent. QTL mapping using pooled-segregant whole-genome sequence analysis with the segregants from the cross of the downgraded parents, revealed several new QTLs. We validated the two most-strongly linked new QTLs by identifying NCS2 and SMD2 as causative genes linked to the superior downgraded parent and we found an allele-specific epistatic interaction between PRP42 and SMD2. Interestingly, the related function of PRP42 and SMD2 suggests an important role for RNA processing in high thermotolerance and underscores the relevance of analyzing minor QTLs. Our results show that identification of minor QTLs involved in complex traits can be successfully accomplished by crossing parent strains that have both been downgraded for a single QTL. This novel approach has the advantage of maintaining all relevant genetic diversity as well as enough phenotypic difference between the parent strains for the trait-of-interest and thus maximizes the chances of successfully identifying additional minor QTLs that are relevant for the phenotypic difference between the original parents.

Most traits of organisms are determined by an interplay of different genes interacting in a complex way. For instance, nearly all industrially-important traits of the yeast Saccharomyces cerevisiae are complex traits. We have analyzed high thermotolerance, which is important for industrial fermentations, reducing cooling costs and sustaining higher productivity. Whereas genetic analysis of complex traits has been cumbersome for many years, the development of pooled-segregant whole-genome sequence analysis now allows successful identification of underlying genetic loci with a major effect. On the other hand, identification of loci with a minor contribution remains a challenge. We now present a methodology for identifying minor loci, which is based on the finding that the inferior parent usually also harbours superior alleles. This allowed construction for the trait of high thermotolerance of two ‘downgraded parent strains’ by replacing in each parent a superior allele by the inferior allele from the other parent. Subsequent mapping with the downgraded parents revealed new minor loci, which we validated by identifying the causative genes. Hence, our results illustrate the power of this methodology for successfully identifying minor loci determining complex traits and with a high chance of being co-responsible for the phenotypic difference between the original parents.

Mapping of quantitative trait loci underlying cold tolerance in rice seedlings via high-throughput sequencing of pooled extremes

Low temperature is a major limiting factor in rice growth and development. Mapping of quantitative trait loci (QTLs) controlling cold tolerance is important for rice breeding. Recent studies have suggested that bulked segregant analysis (BSA) combined with next-generation sequencing (NGS) can be an efficient and cost-effective way for QTL mapping. In this study, we employed NGS-assisted BSA to map QTLs conferring cold tolerance at the seedling stage in rice. By deep sequencing of a pair of large DNA pools acquired from a very large F3 population (10,800 individuals), we obtained ∼450,000 single nucleotide polymorphisms (SNPs) after strict screening. We employed two statistical methods for QTL analysis based on these SNPs, which yielded consistent results. Six QTLs were mapped on chromosomes 1, 2, 5, 8 and 10. The three most significant QTLs on chromosomes 1, 2 and 8 were validated by comparison with previous studies. Two QTLs on chromosomes 2 and 5 were also identified previously, but at the booting stage rather than the seedling stage, suggesting that some QTLs may function at different developmental stages, which would be useful for cold tolerance breeding in rice. Compared with previously reported QTL mapping studies for cold tolerance in rice based on the traditional approaches, the results of this study demonstrated the advantages of NGS-assisted BSA in both efficiency and statistical power.

Development of a D-xylose fermenting and inhibitor tolerant industrial Saccharomyces cerevisiae strain with high performance in lignocellulose hydrolysates using metabolic and evolutionary engineering

BACKGROUND

The production of bioethanol from lignocellulose hydrolysates requires a robust, D-xylose-fermenting and inhibitor-tolerant microorganism as catalyst. The purpose of the present work was to develop such a strain from a prime industrial yeast strain, Ethanol Red, used for bioethanol production.

RESULTS

An expression cassette containing 13 genes including Clostridium phytofermentans XylA, encoding D-xylose isomerase (XI), and enzymes of the pentose phosphate pathway was inserted in two copies in the genome of Ethanol Red. Subsequent EMS mutagenesis, genome shuffling and selection in D-xylose-enriched lignocellulose hydrolysate, followed by multiple rounds of evolutionary engineering in complex medium with D-xylose, gradually established efficient D-xylose fermentation. The best-performing strain, GS1.11-26, showed a maximum specific D-xylose consumption rate of 1.1 g/g DW/h in synthetic medium, with complete attenuation of 35 g/L D-xylose in about 17 h. In separate hydrolysis and fermentation of lignocellulose hydrolysates of Arundo donax (giant reed), spruce and a wheat straw/hay mixture, the maximum specific D-xylose consumption rate was 0.36, 0.23 and 1.1 g/g DW inoculum/h, and the final ethanol titer was 4.2, 3.9 and 5.8% (v/v), respectively. In simultaneous saccharification and fermentation of Arundo hydrolysate, GS1.11-26 produced 32% more ethanol than the parent strain Ethanol Red, due to efficient D-xylose utilization. The high D-xylose fermentation capacity was stable after extended growth in glucose. Cell extracts of strain GS1.11-26 displayed 17-fold higher XI activity compared to the parent strain, but overexpression of XI alone was not enough to establish D-xylose fermentation. The high D-xylose consumption rate was due to synergistic interaction between the high XI activity and one or more mutations in the genome. The GS1.11-26 had a partial respiratory defect causing a reduced aerobic growth rate.

CONCLUSIONS

An industrial yeast strain for bioethanol production with lignocellulose hydrolysates has been developed in the genetic background of a strain widely used for commercial bioethanol production. The strain uses glucose and D-xylose with high consumption rates and partial cofermentation in various lignocellulose hydrolysates with very high ethanol yield. The GS1.11-26 strain shows highly promising potential for further development of an all-round robust yeast strain for efficient fermentation of various lignocellulose hydrolysates.

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.

QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker-assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time-consuming and labor-intensive. Here we report the rapid identification of plant QTLs by whole-genome resequencing of DNAs from two populations each composed of 20-50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL-seq as applied to plant species. We applied QTL-seq to rice recombinant inbred lines and F2 populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL-seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.

Quantitative trait analysis of yeast biodiversity yields novel gene tools for metabolic engineering

Engineering of metabolic pathways by genetic modification has been restricted largely to enzyme-encoding structural genes. The product yield of such pathways is a quantitative genetic trait. Out of 52 Saccharomyces cerevisiae strains phenotyped in small-scale fermentations, we identified strain CBS6412 as having unusually low glycerol production and higher ethanol yield as compared to an industrial reference strain. We mapped the QTLs underlying this quantitative trait with pooled-segregant whole-genome sequencing using 20 superior segregants selected from a total of 257. Plots of SNP variant frequency against SNP chromosomal position revealed one major and one minor locus. Downscaling of the major locus and reciprocal hemizygosity analysis identified an allele of SSK1, ssk1(E330N…K356N), expressing a truncated and partially mistranslated protein, as causative gene. The diploid CBS6412 parent was homozygous for ssk1(E330N…K356N). This allele affected growth and volumetric productivity less than the gene deletion. Introduction of the ssk1(E330N…K356N) allele in the industrial reference strain resulted in stronger reduction of the glycerol/ethanol ratio compared to SSK1 deletion and also compromised volumetric productivity and osmotolerance less. Our results show that polygenic analysis of yeast biodiversity can provide superior novel gene tools for metabolic engineering.

Simultaneous mapping of multiple gene loci with pooled segregants

The analysis of polygenic, phenotypic characteristics such as quantitative traits or inheritable diseases remains an important challenge. It requires reliable scoring of many genetic markers covering the entire genome. The advent of high-throughput sequencing technologies provides a new way to evaluate large numbers of single nucleotide polymorphisms (SNPs) as genetic markers. Combining the technologies with pooling of segregants, as performed in bulked segregant analysis (BSA), should, in principle, allow the simultaneous mapping of multiple genetic loci present throughout the genome. The gene mapping process, applied here, consists of three steps: First, a controlled crossing of parents with and without a trait. Second, selection based on phenotypic screening of the offspring, followed by the mapping of short offspring sequences against the parental reference. The final step aims at detecting genetic markers such as SNPs, insertions and deletions with next generation sequencing (NGS). Markers in close proximity of genomic loci that are associated to the trait have a higher probability to be inherited together. Hence, these markers are very useful for discovering the loci and the genetic mechanism underlying the characteristic of interest. Within this context, NGS produces binomial counts along the genome, i.e., the number of sequenced reads that matches with the SNP of the parental reference strain, which is a proxy for the number of individuals in the offspring that share the SNP with the parent. Genomic loci associated with the trait can thus be discovered by analyzing trends in the counts along the genome. We exploit the link between smoothing splines and generalized mixed models for estimating the underlying structure present in the SNP scatterplots.

Quantitative 1H-NMR-metabolomics reveals extensive metabolic reprogramming and the effect of the aquaglyceroporin FPS1 in ethanol-stressed yeast cells

A metabolomic analysis using high resolution ¹H NMR spectroscopy coupled with multivariate statistical analysis was used to characterize the alterations in the endo- and exo-metabolome of S. cerevisiae BY4741 during the exponential phase of growth in minimal medium supplemented with different ethanol concentrations (0, 2, 4 and 6% v/v). This study provides evidence that supports the notion that ethanol stress induces reductive stress in yeast cells, which, in turn, appears to be counteracted by the increase in the rate of NAD⁺ regenerating bioreactions. Metabolomics data also shows increased intra- and extra-cellular accumulation of most amino acids and TCA cycle intermediates in yeast cells growing under ethanol stress suggesting a state of overflow metabolism in turn of the pyruvate branch-point. Given its previous implication in ethanol stress resistance in yeast, this study also focused on the effect of the expression of the aquaglyceroporin encoded by FPS1 in the yeast metabolome, in the absence or presence of ethanol stress. The metabolomics data collected herein shows that the deletion of the FPS1 gene in the absence of ethanol stress partially mimics the effect of ethanol stress in the parental strain. Moreover, the results obtained suggest that the reported action of Fps1 in mediating the passive diffusion of glycerol is a key factor in the maintenance of redox balance, an important feature for ethanol stress resistance, and may interfere with the ability of the yeast cell to accumulate trehalose. Overall, the obtained results corroborate the idea that metabolomic approaches may be crucial tools to understand the function and/or the effect of membrane transporters/porins, such as Fps1, and may be an important tool for the clear-cut design of improved process conditions and more robust yeast strains aiming to optimize industrial fermentation performance.

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.

Advances in quantitative trait analysis in yeast

Understanding the genetic mechanisms underlying complex traits is one of the next frontiers in biology. The budding yeast Saccharomyces cerevisiae has become an important model for elucidating the mechanisms that govern natural genetic and phenotypic variation. This success is partially due to its intrinsic biological features, such as the short sexual generation time, high meiotic recombination rate, and small genome size. Precise reverse genetics technologies allow the high throughput manipulation of genetic information with exquisite precision, offering the unique opportunity to experimentally measure the phenotypic effect of genetic variants. Population genomic and phenomic studies have revealed widespread variation between diverged populations, characteristic of man-made environments, as well as geographic clusters of wild strains along with naturally occurring recombinant strains (mosaics). Here, we review these recent studies and provide a perspective on how these previously unappreciated levels of variation can help to bridge our understanding of the genotype-phenotype gap, keeping budding yeast at the forefront of genetic studies. Not only are quantitative trait loci (QTL) being mapped with high resolution down to the nucleotide, for the first time QTLs of modest effect and complex interactions between these QTLs and between QTLs and the environment are being determined experimentally at unprecedented levels using next generation techniques of deep sequencing selected pools of individuals as well as multi-generational crosses.