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

Gene-gene and gene-environment interactions in complex traits in yeast

One of the fundamental questions in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that, in most cases, the effect of a genetic locus on the phenotype is context-dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the studies, in both model organisms and humans, that map the genetic regulation of phenotypic variation in complex traits primarily identify additive loci with independent effects. This does not reflect an absence of the contribution of genetic interactions to phenotypic variation, but instead is a consequence of the technical limitations in mapping gene-gene interactions (GGI) and gene-environment interactions (GEI). Yeast, with its detailed molecular understanding, diverse population genomics and ease of genetic manipulation, is a unique and powerful resource to study the contributions of GGI and GEI in the regulation of phenotypic variation. Here we review studies in yeast that have identified GGI and GEI that regulate phenotypic variation, and discuss the contribution of these findings in explaining missing heritability of complex traits, and how observations from these GGI and GEI studies enhance our understanding of the mechanisms underlying genetic robustness and adaptability that shape the architecture of the genotype-phenotype map.

Enhanced Wort Fermentation with De Novo Lager Hybrids Adapted to High-Ethanol Environments

Interspecific hybridization is a valuable tool for developing and improving brewing yeast in a number of industry-relevant aspects. However, the genomes of newly formed hybrids can be unstable. Here, we exploited this trait by adapting four brewing yeast strains, three of which were de novo interspecific lager hybrids with different ploidy levels, to high ethanol concentrations in an attempt to generate variant strains with improved fermentation performance in high-gravity wort. Through a batch fermentation-based adaptation process and selection based on a two-step screening process, we obtained eight variant strains which we compared to the wild-type strains in 2-liter-scale wort fermentations replicating industrial conditions. The results revealed that the adapted variants outperformed the strains from which they were derived, and the majority also possessed several desirable brewing-relevant traits, such as increased ester formation and ethanol tolerance, as well as decreased diacetyl formation. The variants obtained from the polyploid hybrids appeared to show greater improvements in fermentation performance than those derived from diploid strains. Interestingly, it was not only the hybrid strains, but also the Saccharomyces cerevisiae parent strain, that appeared to adapt and showed considerable changes in genome size. Genome sequencing and ploidy analysis revealed that changes had occurred at both the chromosome and single nucleotide levels in all variants. Our study demonstrates the possibility of improving de novo lager yeast hybrids through adaptive evolution by generating stable and superior variants that possess traits relevant to industrial lager beer fermentation.IMPORTANCE Recent studies have shown that hybridization is a valuable tool for creating new and diverse strains of lager yeast. Adaptive evolution is another strain development tool that can be applied in order to improve upon desirable traits. Here, we apply adaptive evolution to newly created lager yeast hybrids by subjecting them to environments containing high ethanol levels. We isolated and characterized a number of adapted variants which possess improved fermentation properties and ethanol tolerance. Genome analysis revealed substantial changes in the variants compared to the original strains. These improved variant strains were produced without any genetic modification and are suitable for industrial lager beer fermentations.

A multi-phase approach to select new wine yeast strains with enhanced fermentative fitness and glutathione production

The genetic improvement of winemaking yeasts is a virtually infinite process, as the design of new strains must always cope with varied and ever-evolving production contexts. Good wine yeasts must feature both good primary traits, which are related to the overall fermentative fitness of the strain, and secondary traits, which provide accessory features augmenting its technological value. In this context, the superiority of "blind," genetic improvement techniques, as those based on the direct selection of the desired phenotype without prior knowledge of the genotype, was widely proven. Blind techniques such as adaptive evolution strategies were implemented for the enhancement of many traits of interest in the winemaking field. However, these strategies usually focus on single traits: this possibly leads to genetic tradeoff phenomena, where the selection of enhanced secondary traits might lead to sub-optimal primary fermentation traits. To circumvent this phenomenon, we applied a multi-step and strongly directed genetic improvement strategy aimed at combining a strong fermentative aptitude (primary trait) with an enhanced production of glutathione (secondary trait). We exploited the random genetic recombination associated to a library of 69 monosporic clones of strain UMCC 855 (Saccharomyces cerevisiae) to search for new candidates possessing both traits. This was achieved by consecutively applying three directional selective criteria: molybdate resistance (1), fermentative aptitude (2), and glutathione production (3). The strategy brought to the selection of strain 21T2-D58, which produces a high concentration of glutathione, comparable to that of other glutathione high-producers, still with a much greater fermentative aptitude.

Identification of Novel Alleles Conferring Superior Production of Rose Flavor Phenylethyl Acetate Using Polygenic Analysis in Yeast

Flavor compound metabolism is one of the last areas in metabolism where multiple genes encoding biosynthetic enzymes are still unknown. A major challenge is the involvement of side activities of enzymes having their main function in other areas of metabolism. We have applied pooled-segregant whole-genome sequence analysis to identify novel Saccharomyces cerevisiae genes affecting production of phenylethyl acetate (2-PEAc). This is a desirable flavor compound of major importance in alcoholic beverages imparting rose- and honey-like aromas, with production of high 2-PEAc levels considered a superior trait. Four quantitative trait loci (QTLs) responsible for high 2-PEAc production were identified, with two loci each showing linkage to the genomes of the BTC.1D and ER18 parents. The first two loci were investigated further. The causative genes were identified by reciprocal allele swapping into both parents using clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9. The superior allele of the first major causative gene, FAS2, was dominant and contained two unique single nucleotide polymorphisms (SNPs) responsible for high 2-PEAc production that were not present in other sequenced yeast strains. FAS2 encodes the alpha subunit of the fatty acid synthetase complex. Surprisingly, the second causative gene was a mutant allele of TOR1, a gene involved in nitrogen regulation. Exchange of both superior alleles in the ER18 parent strain increased 2-PEAc production 70%, nearly to the same level as in the best superior segregant. Our results show that polygenic analysis combined with CRISPR/Cas9-mediated allele exchange is a powerful tool for identification of genes encoding missing metabolic enzymes and for development of industrial yeast strains generating novel flavor profiles in alcoholic beverages.IMPORTANCE Multiple reactions in flavor metabolism appear to be catalyzed by side activities of other enzymes that have been difficult to identify. We have applied genetic mapping of quantitative trait loci in the yeast Saccharomyces cerevisiae to identify mutant alleles of genes determining the production of phenylethyl acetate, an important flavor compound imparting rose- and honey-like aromas to alcoholic beverages. We identified a unique, dominant allele of FAS2 that supports high production of phenylethyl acetate. FAS2 encodes a subunit of the fatty acid synthetase complex and apparently exerts an important side activity on one or more alternative substrates in flavor compound synthesis. The second mutant allele contained a nonsense mutation in TOR1, a gene involved in nitrogen regulation of growth. Together the two alleles strongly increased the level of phenylethyl acetate. Our work highlights the potential of genetic mapping of quantitative phenotypic traits to identify novel enzymes and regulatory components in yeast metabolism, including regular metabolic enzymes with unknown side activities responsible for biosynthesis of specific flavor compounds. The superior alleles identified can be used to develop industrial yeast strains generating novel flavor profiles in alcoholic beverages.

Genomic-based-breeding tools for tropical maize improvement

Maize has traditionally been the main staple diet in the Southern Asia and Sub-Saharan Africa and widely grown by millions of resource poor small scale farmers. Approximately, 35.4 million hectares are sown to tropical maize, constituting around 59% of the developing worlds. Tropical maize encounters tremendous challenges besides poor agro-climatic situations with average yields recorded <3 tones/hectare that is far less than the average of developed countries. On the contrary to poor yields, the demand for maize as food, feed, and fuel is continuously increasing in these regions. Heterosis breeding introduced in early 90 s improved maize yields significantly, but genetic gains is still a mirage, particularly for crop growing under marginal environments. Application of molecular markers has accelerated the pace of maize breeding to some extent. The availability of array of sequencing and genotyping technologies offers unrivalled service to improve precision in maize-breeding programs through modern approaches such as genomic selection, genome-wide association studies, bulk segregant analysis-based sequencing approaches, etc. Superior alleles underlying complex traits can easily be identified and introgressed efficiently using these sequence-based approaches. Integration of genomic tools and techniques with advanced genetic resources such as nested association mapping and backcross nested association mapping could certainly address the genetic issues in maize improvement programs in developing countries. Huge diversity in tropical maize and its inherent capacity for doubled haploid technology offers advantage to apply the next generation genomic tools for accelerating production in marginal environments of tropical and subtropical world. Precision in phenotyping is the key for success of any molecular-breeding approach. This article reviews genomic technologies and their application to improve agronomic traits in tropical maize breeding has been reviewed in detail.

Physiology, ecology and industrial applications of aroma formation in yeast

Yeast cells are often employed in industrial fermentation processes for their ability to efficiently convert relatively high concentrations of sugars into ethanol and carbon dioxide. Additionally, fermenting yeast cells produce a wide range of other compounds, including various higher alcohols, carbonyl compounds, phenolic compounds, fatty acid derivatives and sulfur compounds. Interestingly, many of these secondary metabolites are volatile and have pungent aromas that are often vital for product quality. In this review, we summarize the different biochemical pathways underlying aroma production in yeast as well as the relevance of these compounds for industrial applications and the factors that influence their production during fermentation. Additionally, we discuss the different physiological and ecological roles of aroma-active metabolites, including recent findings that point at their role as signaling molecules and attractants for insect vectors.

Mapping Quantitative Trait Loci in Yeast

Natural Saccharomyces strains isolated from the wild differ quantitatively in molecular and organismal phenotypes. Quantitative trait loci (QTL) mapping is a powerful approach for identifying sequence variants that alter gene function. In yeast, QTL mapping has been used in designed crosses to map functional polymorphisms. This approach, outlined here, is often the first step in understanding the molecular basis of quantitative traits. New large-scale sequencing surveys have the potential to directly associate genotypes with organismal phenotypes, providing a broader catalog of causative genetic variants. Additional analysis of intermediate phenotypes (e.g., RNA, protein, or metabolite levels) can produce a multilayered and integrated view of individual variation, producing a high-resolution view of the genotype-phenotype map.

Saccharomyces cerevisiae strains for second-generation ethanol production: from academic exploration to industrial implementation

The recent start-up of several full-scale 'second generation' ethanol plants marks a major milestone in the development of Saccharomyces cerevisiae strains for fermentation of lignocellulosic hydrolysates of agricultural residues and energy crops. After a discussion of the challenges that these novel industrial contexts impose on yeast strains, this minireview describes key metabolic engineering strategies that have been developed to address these challenges. Additionally, it outlines how proof-of-concept studies, often developed in academic settings, can be used for the development of robust strain platforms that meet the requirements for industrial application. Fermentation performance of current engineered industrial S. cerevisiae strains is no longer a bottleneck in efforts to achieve the projected outputs of the first large-scale second-generation ethanol plants. Academic and industrial yeast research will continue to strengthen the economic value position of second-generation ethanol production by further improving fermentation kinetics, product yield and cellular robustness under process conditions.

Budding Yeast Strains and Genotype-Phenotype Mapping

A small number of well-studied laboratory strains of Saccharomyces cerevisiae, mostly derived from S288C, are used in yeast research. Although powerful, studies for understanding S288C do not always capture the phenotypic essence or the genetic complexity of S. cerevisiae biology. This is particularly problematic for multilocus phenotypes identified in laboratory strains because these loci have never been jointly exposed to natural selection and the corresponding phenotypes do not represent optimization for any particular purpose or environment. Isolation and sequencing of new natural yeast strains also reveal that the total sequence diversity of the S. cerevisiae global population is poorly sampled in common laboratory strains. Here we discuss methodologies required for using the natural genetic variation in yeast to complete a genotype-phenotype map.

Engineering tolerance to industrially relevant stress factors in yeast cell factories

The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way.

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally occurring variants of HXT7 Our study highlights the complex nature of the genotype-to-phenotype map within and between environments.

Omics analysis of acetic acid tolerance in Saccharomyces cerevisiae

Acetic acid is an inhibitor in industrial processes such as wine making and bioethanol production from cellulosic hydrolysate. It causes energy depletion, inhibition of metabolic enzyme activity, growth arrest and ethanol productivity losses in Saccharomyces cerevisiae. Therefore, understanding the mechanisms of the yeast responses to acetic acid stress is essential for improving acetic acid tolerance and ethanol production. Although 329 genes associated with acetic acid tolerance have been identified in the Saccharomyces genome and included in the database ( http://www.yeastgenome.org/observable/resistance_to_acetic_acid/overview ), the cellular mechanistic responses to acetic acid remain unclear in this organism. Post-genomic approaches such as transcriptomics, proteomics, metabolomics and chemogenomics are being applied to yeast and are providing insight into the mechanisms and interactions of genes, proteins and other components that together determine complex quantitative phenotypic traits such as acetic acid tolerance. This review focuses on these omics approaches in the response to acetic acid in S. cerevisiae. Additionally, several novel strains with improved acetic acid tolerance have been engineered by modifying key genes, and the application of these strains and recently acquired knowledge to industrial processes is also discussed.

Quantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait

Engineering complex phenotypes for industrial and synthetic biology applications is difficult and often confounds rational design. Bioethanol production from lignocellulosic feedstocks is a complex trait that requires multiple host systems to utilize, detoxify, and metabolize a mixture of sugars and inhibitors present in plant hydrolysates. Here, we demonstrate an integrated approach to discovering and optimizing host factors that impact fitness of Saccharomyces cerevisiae during fermentation of a Miscanthus x giganteus plant hydrolysate. We first used high-resolution Quantitative Trait Loci (QTL) mapping and systematic bulk Reciprocal Hemizygosity Analysis (bRHA) to discover 17 loci that differentiate hydrolysate tolerance between an industrially related (JAY291) and a laboratory (S288C) strain. We then used this data to identify a subset of favorable allelic loci that were most amenable for strain engineering. Guided by this "genetic blueprint", and using a dual-guide Cas9-based method to efficiently perform multikilobase locus replacements, we engineered an S288C-derived strain with superior hydrolysate tolerance than JAY291. Our methods should be generalizable to engineering any complex trait in S. cerevisiae, as well as other organisms.

Genetic Causes of Phenotypic Adaptation to the Second Fermentation of Sparkling Wines in Saccharomyces cerevisiae

Hybridization is known to improve complex traits due to heterosis and phenotypic robustness. However, these phenomena have been rarely explained at the molecular level. Here, the genetic determinism of Saccharomyces cerevisiae fermentation performance was investigated using a QTL mapping approach on an F1-progeny population. Three main QTL were detected, with positive alleles coming from both parental strains. The heterosis effect found in the hybrid was partially explained by three loci showing pseudooverdominance and dominance effects. The molecular dissection of those QTL revealed that the adaptation to second fermentation is related to pH, lipid, or osmotic regulation. Our results suggest that the stressful conditions of second fermentation have driven the selection of rare genetic variants adapted to maintain yeast cell homeostasis and, in particular, to low pH conditions.

Mitochondrial Superoxide Dismutase and Yap1p Act as a Signaling Module Contributing to Ethanol Tolerance of the Yeast Saccharomyces cerevisiae

There are two superoxide dismutases in the yeast Saccharomyces cerevisiae-cytoplasmic and mitochondrial enzymes. Inactivation of the cytoplasmic enzyme, Sod1p, renders the cells sensitive to a variety of stresses, while inactivation of the mitochondrial isoform, Sod2p, typically has a weaker effect. One exception is ethanol-induced stress. Here we studied the role of Sod2p in ethanol tolerance of yeast. First, we found that repression of SOD2 prevents ethanol-induced relocalization of yeast hydrogen peroxide-sensing transcription factor Yap1p, one of the key stress resistance proteins. In agreement with this, the levels of Trx2p and Gsh1p, proteins encoded by Yap1 target genes, were decreased in the absence of Sod2p. Analysis of the ethanol sensitivities of the cells lacking Sod2p, Yap1p, or both indicated that the two proteins act in the same pathway. Moreover, preconditioning with hydrogen peroxide restored the ethanol resistance of yeast cells with repressed SOD2 Interestingly, we found that mitochondrion-to-nucleus signaling by Rtg proteins antagonizes Yap1p activation. Together, our data suggest that hydrogen peroxide produced by Sod2p activates Yap1p and thus plays a signaling role in ethanol tolerance.

IMPORTANCE

Baker's yeast harbors multiple systems that ensure tolerance to high concentrations of ethanol. Still, the role of mitochondria under severe ethanol stress in yeast is not completely clear. Our study revealed a signaling function of mitochondria which contributes significantly to the ethanol tolerance of yeast cells. We found that mitochondrial superoxide dismutase Sod2p and cytoplasmic hydrogen peroxide sensor Yap1p act together as a module of the mitochondrion-to-nucleus signaling pathway. We also report cross talk between this pathway and the conventional retrograde signaling cascade activated by dysfunctional mitochondria.

The sole introduction of two single-point mutations establishes glycerol utilization in Saccharomyces cerevisiae CEN.PK derivatives

BACKGROUND

Glycerol is an abundant by-product of biodiesel production and has several advantages as a substrate in biotechnological applications. Unfortunately, the popular production host Saccharomyces cerevisiae can barely metabolize glycerol by nature.

RESULTS

In this study, two evolved derivatives of the strain CEN.PK113-1A were created that were able to grow in synthetic glycerol medium (strains PW-1 and PW-2). Their growth performances on glycerol were compared with that of the previously published evolved CEN.PK113-7D derivative JL1. As JL1 showed a higher maximum specific growth rate on glycerol (0.164 h^-1 compared to 0.119 h^-1 for PW-1 and 0.127 h^-1 for PW-2), its genomic DNA was subjected to whole-genome resequencing. Two point mutations in the coding sequences of the genes UBR2 and GUT1 were identified to be crucial for growth in synthetic glycerol medium and subsequently verified by reverse engineering of the wild-type strain CEN.PK113-7D. The growth rate of the resulting reverse-engineered strain was 0.130 h^-1. Sanger sequencing of the GUT1 and UBR2 alleles of the above-mentioned evolved strains PW-1 and PW-2 also revealed one single-point mutation in these two genes, and both mutations were demonstrated to be also crucial and sufficient for obtaining a maximum specific growth rate on glycerol of ~0.120 h^-1.

CONCLUSIONS

The current work confirmed the importance of UBR2 and GUT1 as targets for establishing glycerol utilization in strains of the CEN.PK family. In addition, it shows that a growth rate on glycerol of 0.130 h^-1 can be established in reverse-engineered CEN.PK strains by solely replacing a single amino acid in the coding sequences of both Ubr2 and Gut1.

Rapid identification of QTLs underlying resistance to Cucumber mosaic virus in pepper (Capsicum frutescens)

Next-generation sequencing enabled a fast discovery of QTLs controlling CMV resistant in pepper. The gene CA02g19570 as a possible candidate gene of qCmr2.1 was identified for resistance to CMV in pepper. Cucumber mosaic virus (CMV) is one of the most important viruses infecting pepper, but the genetic basis of CMV resistance in pepper is elusive. In this study, we identified a candidate gene for CMV resistance QTL, qCmr2.1 through SLAF-seq. Segregation analysis in F₂, BC₁ and F_2:3 populations derived from a cross between two inbred lines 'PBC688' (CMV-resistant) and 'G29' (CMV-susceptible) suggested quantitative inheritance of resistance to CMV in pepper. Genome-wide comparison of SNP profiles between the CMV-resistant and CMV-susceptible bulks constructed from an F₂ population identified two QTLs, designated as qCmr2.1 on chromosome 2 and qCmr11.1 on chromosome 11 for resistance to CMV in PBC688, which were confirmed by InDel marker-based classical QTL mapping in the F₂ population. As a major QTL, joint SLAF-seq and traditional QTL analysis delimited qCmr2.1 to a 330 kb genomic region. Two pepper genes, CA02g19570 and CA02g19600, were identified in this region, which are homologous with the genes LOC104113703, LOC104248995, LOC102603934 and LOC101248357, which were predicted to encode N-like protein associated with TMV-resistant in Solanum crops. Quantitative RT-PCR revealed higher expression levels of CA02g19570 in CMV resistance genotypes. The CA02g19600 did not exhibit obvious regularity in expression patterns. Higher relative expression levels of CA02g19570 in PBC688 and F₁ were compared with those in G29 during days after inoculation. These results provide support for CA02g19570 as a possible candidate gene of qCmr2.1 for resistance to CMV in pepper.

Natural gene expression variation studies in yeast

The rise of sequence information across different yeast species and strains is driving an increasing number of studies in the emerging field of genomics to associate polymorphic variants, mRNA abundance and phenotypic differences between individuals. Here, we gathered evidence from recent studies covering several layers that define the genotype-phenotype gap, such as mRNA abundance, allele-specific expression and translation efficiency to demonstrate how genetic variants co-evolve and define an individual's genome. Moreover, we exposed several antecedents where inter- and intra-specific studies led to opposite conclusions, probably owing to genetic divergence. Future studies in this area will benefit from the access to a massive array of well-annotated genomes and new sequencing technologies, which will allow the fine breakdown of the complex layers that delineate the genotype-phenotype map. Copyright © 2016 John Wiley & Sons, Ltd.

Fitness Trade-Offs Lead to Suppressor Tolerance in Yeast

Genetic variation among individuals within a population provides the raw material for phenotypic diversity upon which natural selection operates. Some given variants can act on multiple standing genomic variations simultaneously and release previously inaccessible phenotypes, leading to increased adaptive potential upon challenging environments. Previously, we identified such a variant related to a tRNA nonsense suppressor in yeast. When introduced into other genetic backgrounds, the suppressor led to an increased population phenotypic variance on various culture conditions, conferring background and environment specific selective advantages. Nonetheless, most isolates are intolerant to the suppressor on rich media due to a severe fitness cost. Here, we found that the tolerance to suppressor is related to a surprising level of fitness outburst, showing a trade-off effect to accommodate the cost of carrying the suppressor. To dissect the genetic basis of such trade-offs, we crossed strains with contrasting tolerance levels on rich media, and analyzed the fitness distribution patterns in the offspring. Combining quantitative tetrad analysis and bulk segregant analysis, we identified two genes, namely MKT1 and RGA1, involved in suppressor tolerance. We showed that alleles from the tolerant parent for both genes conferred a significant gain of fitness, which increased the suppressor tolerance. Our results present a detailed dissection of suppressor tolerance in yeast and provide insights into the molecular basis of trade-offs between fitness and evolutionary potential.

Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

A wealth of biochemical and molecular data have been reported regarding ethanol toxicity in the yeast Saccharomyces cerevisiae However, direct physical data on the effects of ethanol stress on yeast cells are almost nonexistent. This lack of information can now be addressed by using atomic force microscopy (AFM) technology. In this report, we show that the stiffness of glucose-grown yeast cells challenged with 9% (vol/vol) ethanol for 5 h was dramatically reduced, as shown by a 5-fold drop of Young's modulus. Quite unexpectedly, a mutant deficient in the Msn2/Msn4 transcription factor, which is known to mediate the ethanol stress response, exhibited a low level of stiffness similar to that of ethanol-treated wild-type cells. Reciprocally, the stiffness of yeast cells overexpressing MSN2 was about 35% higher than that of the wild type but was nevertheless reduced 3- to 4-fold upon exposure to ethanol. Based on these and other data presented herein, we postulated that the effect of ethanol on cell stiffness may not be mediated through Msn2/Msn4, even though this transcription factor appears to be a determinant in the nanomechanical properties of the cell wall. On the other hand, we found that as with ethanol, the treatment of yeast with the antifungal amphotericin B caused a significant reduction of cell wall stiffness. Since both this drug and ethanol are known to alter, albeit by different means, the fluidity and structure of the plasma membrane, these data led to the proposition that the cell membrane contributes to the biophysical properties of yeast cells.

IMPORTANCE

Ethanol is the main product of yeast fermentation but is also a toxic compound for this process. Understanding the mechanism of this toxicity is of great importance for industrial applications. While most research has focused on genomic studies of ethanol tolerance, we investigated the effects of ethanol at the biophysical level and found that ethanol causes a strong reduction of the cell wall rigidity (or stiffness). We ascribed this effect to the action of ethanol perturbing the cell membrane integrity and hence proposed that the cell membrane contributes to the cell wall nanomechanical properties.

EXPLoRA-web: linkage analysis of quantitative trait loci using bulk segregant analysis

Identification of genomic regions associated with a phenotype of interest is a fundamental step toward solving questions in biology and improving industrial research. Bulk segregant analysis (BSA) combined with high-throughput sequencing is a technique to efficiently identify these genomic regions associated with a trait of interest. However, distinguishing true from spuriously linked genomic regions and accurately delineating the genomic positions of these truly linked regions requires the use of complex statistical models currently implemented in software tools that are generally difficult to operate for non-expert users. To facilitate the exploration and analysis of data generated by bulked segregant analysis, we present EXPLoRA-web, a web service wrapped around our previously published algorithm EXPLoRA, which exploits linkage disequilibrium to increase the power and accuracy of quantitative trait loci identification in BSA analysis. EXPLoRA-web provides a user friendly interface that enables easy data upload and parallel processing of different parameter configurations. Results are provided graphically and as BED file and/or text file and the input is expected in widely used formats, enabling straightforward BSA data analysis. The web server is available at http://bioinformatics.intec.ugent.be/explora-web/.

Exploiting budding yeast natural variation for industrial processes

For the last two decades, the natural variation of the yeast Saccharomyces cerevisiae has been massively exploited with the aim of understanding ecological and evolutionary processes. As a result, many new genetic variants have been uncovered, providing a large catalogue of alleles underlying complex traits. These alleles represent a rich genetic resource with the potential to provide new strains that can cope with the growing demands of industrial fermentation processes. When surveyed in detail, several of these variants have proven useful in wine and beer industries by improving nitrogen utilisation, fermentation kinetics, ethanol production, sulphite resistance and aroma production. Here, I illustrate how allele-specific expression and polymorphisms within the coding region of GDB1 underlie fermentation kinetic differences in synthetic wine must. Nevertheless, the genetic basis of how GDB1 variants and other natural alleles interact in foreign genetic backgrounds remains unclear. Further studies in large sets of strains, recombinant hybrids and multiple parental pairs will broaden our knowledge of the molecular and genetic basis of trait adaptation for utilisation in applied and industrial processes.

Mapping QTLs for Salt Tolerance in Rice (Oryza sativa L.) by Bulked Segregant Analysis of Recombinant Inbred Lines Using 50K SNP Chip

Soil salinity is a major constraint to rice production in large inland and coastal areas around the world. Modern high yielding rice varieties are particularly sensitive to high salt stress. There are salt tolerant landraces and traditional varieties of rice but with limited information on genomic regions (QTLs) and genes responsible for their tolerance. Here we describe a method for rapid identification of QTLs for reproductive stage salt tolerance in rice using bulked segregant analysis (BSA) of bi-parental recombinant inbred lines (RIL). The number of RILs required for the creation of two bulks with extreme phenotypes was optimized to be thirty each. The parents and bulks were genotyped using a 50K SNP chip to identify genomic regions showing homogeneity for contrasting alleles of polymorphic SNPs in the two bulks. The method was applied to ‘CSR11/MI48’ RILs segregating for reproductive stage salt tolerance. Genotyping of the parents and RIL bulks, made on the basis of salt sensitivity index for grain yield, revealed 6,068 polymorphic SNPs and 21 QTL regions showing homogeneity of contrasting alleles in the two bulks. The method was validated further with ‘CSR27/MI48’ RILs used earlier for mapping salt tolerance QTLs using low-density SSR markers. BSA with 50K SNP chip revealed 5,021 polymorphic loci and 34 QTL regions. This not only confirmed the location of previously mapped QTLs but also identified several new QTLs, and provided a rapid way to scan the whole genome for mapping QTLs for complex agronomic traits in rice.

Genomic saturation mutagenesis and polygenic analysis identify novel yeast genes affecting ethyl acetate production, a non-selectable polygenic trait

Isolation of mutants in populations of microorganisms has been a valuable tool in experimental genetics for decades. The main disadvantage, however, is the inability of isolating mutants in non-selectable polygenic traits. Most traits of organisms, however, are non-selectable and polygenic, including industrially important properties of microorganisms. The advent of powerful technologies for polygenic analysis of complex traits has allowed simultaneous identification of multiple causative mutations among many thousands of irrelevant mutations. We now show that this also applies to haploid strains of which the genome has been loaded with induced mutations so as to affect as many non-selectable, polygenic traits as possible. We have introduced about 900 mutations into single haploid yeast strains using multiple rounds of EMS mutagenesis, while maintaining the mating capacity required for genetic mapping. We screened the strains for defects in flavor production, an important non-selectable, polygenic trait in yeast alcoholic beverage production. A haploid strain with multiple induced mutations showing reduced ethyl acetate production in semi-anaerobic fermentation, was selected and the underlying quantitative trait loci (QTLs) were mapped using pooled-segregant whole-genome sequence analysis after crossing with an unrelated haploid strain. Reciprocal hemizygosity analysis and allele exchange identified PMA1 and CEM1 as causative mutant alleles and TPS1 as a causative genetic background allele. The case of CEM1 revealed that relevant mutations without observable effect in the haploid strain with multiple induced mutations (in this case due to defective mitochondria) can be identified by polygenic analysis as long as the mutations have an effect in part of the segregants (in this case those that regained fully functional mitochondria). Our results show that genomic saturation mutagenesis combined with complex trait polygenic analysis could be used successfully to identify causative alleles underlying many non-selectable, polygenic traits in small collections of haploid strains with multiple induced mutations.

How do yeast cells become tolerant to high ethanol concentrations?

The brewer's yeast Saccharomyces cerevisiae displays a much higher ethanol tolerance compared to most other organisms, and it is therefore commonly used for the industrial production of bioethanol and alcoholic beverages. However, the genetic determinants underlying this yeast's exceptional ethanol tolerance have proven difficult to elucidate. In this perspective, we discuss how different types of experiments have contributed to our understanding of the toxic effects of ethanol and the mechanisms and complex genetics underlying ethanol tolerance. In a second part, we summarize the different routes and challenges involved in obtaining superior industrial yeasts with improved ethanol tolerance.

Polygenic analysis and targeted improvement of the complex trait of high acetic acid tolerance in the yeast Saccharomyces cerevisiae

BACKGROUND

Acetic acid is one of the major inhibitors in lignocellulose hydrolysates used for the production of second-generation bioethanol. Although several genes have been identified in laboratory yeast strains that are required for tolerance to acetic acid, the genetic basis of the high acetic acid tolerance naturally present in some Saccharomyces cerevisiae strains is unknown. Identification of its polygenic basis may allow improvement of acetic acid tolerance in yeast strains used for second-generation bioethanol production by precise genome editing, minimizing the risk of negatively affecting other industrially important properties of the yeast.

RESULTS

Haploid segregants of a strain with unusually high acetic acid tolerance and a reference industrial strain were used as superior and inferior parent strain, respectively. After crossing of the parent strains, QTL mapping using the SNP variant frequency determined by pooled-segregant whole-genome sequence analysis revealed two major QTLs. All F1 segregants were then submitted to multiple rounds of random inbreeding and the superior F7 segregants were submitted to the same analysis, further refined by sequencing of individual segregants and bioinformatics analysis taking into account the relative acetic acid tolerance of the segregants. This resulted in disappearance in the QTL mapping with the F7 segregants of a major F1 QTL, in which we identified HAA1, a known regulator of high acetic acid tolerance, as a true causative allele. Novel genes determining high acetic acid tolerance, GLO1, DOT5, CUP2, and a previously identified component, VMA7, were identified as causative alleles in the second major F1 QTL and in three newly appearing F7 QTLs, respectively. The superior HAA1 allele contained a unique single point mutation that significantly improved acetic acid tolerance under industrially relevant conditions when inserted into an industrial yeast strain for second-generation bioethanol production.

CONCLUSIONS

This work reveals the polygenic basis of high acetic acid tolerance in S. cerevisiae in unprecedented detail. It also shows for the first time that a single strain can harbor different sets of causative genes able to establish the same polygenic trait. The superior alleles identified can be used successfully for improvement of acetic acid tolerance in industrial yeast strains.

Bulk Segregant Analysis Reveals the Genetic Basis of a Natural Trait Variation in Fission Yeast

Although the fission yeast Schizosaccharomyces pombe is a well-established model organism, studies of natural trait variations in this species remain limited. To assess the feasibility of segregant-pool-based mapping of phenotype-causing genes in natural strains of fission yeast, we investigated the cause of a maltose utilization defect (Mal(-)) of the S. pombe strain CBS5557 (originally known as Schizosaccharomyces malidevorans). Analyzing the genome sequence of CBS5557 revealed 955 nonconservative missense substitutions, and 61 potential loss-of-function variants including 47 frameshift indels, 13 early stop codons, and 1 splice site mutation. As a side benefit, our analysis confirmed 146 sequence errors in the reference genome and improved annotations of 27 genes. We applied bulk segregant analysis to map the causal locus of the Mal(-) phenotype. Through sequencing the segregant pools derived from a cross between CBS5557 and the laboratory strain, we located the locus to within a 2.23-Mb chromosome I inversion found in most S. pombe isolates including CBS5557. To map genes within the inversion region that occupies 18% of the genome, we created a laboratory strain containing the same inversion. Analyzing segregants from a cross between CBS5557 and the inversion-containing laboratory strain narrowed down the locus to a 200-kb interval and led us to identify agl1, which suffers a 5-bp deletion in CBS5557, as the causal gene. Interestingly, loss of agl1 through a 34-kb deletion underlies the Mal(-) phenotype of another S. pombe strain CGMCC2.1628. This work adapts and validates the bulk segregant analysis method for uncovering trait-gene relationship in natural fission yeast strains.

Adaptation to High Ethanol Reveals Complex Evolutionary Pathways

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts.

Organisms can evolve resistance to specific stress factors, which allows them to thrive in environments where non-adapted organisms fail to grow. However, the molecular mechanisms that underlie adaptation to complex stress factors that interfere with basic cellular processes are poorly understood. In this study, we reveal how yeast populations adapt to high ethanol concentrations, an ecologically and industrially relevant stress that is still poorly understood. We exposed six independent populations of genetically identical yeast cells to gradually increasing ethanol levels, and we monitored the changes in their DNA sequence over a two-year period. Together with novel computational analyses, we could identify the mutational dynamics and molecular mechanisms underlying increased ethanol resistance. Our results show how adaptation to high ethanol is complex and can be reached through different mutational pathways. Together, our study offers a detailed picture of how populations adapt to a complex continuous stress and identifies several mutations that increase ethanol resistance, which opens new routes to obtain superior biofuel yeast strains.

Comparative quantitative trait loci for silique length and seed weight in Brassica napus

Silique length (SL) and seed weight (SW) are important yield-associated traits in rapeseed (Brassica napus). Although many quantitative trait loci (QTL) for SL and SW have been identified in B. napus, comparative analysis for those QTL is seldom performed. In the present study, 20 and 21 QTL for SL and SW were identified in doubled haploid (DH) and DH-derived reconstructed F2 populations in rapeseed, explaining 55.1-74.3% and 24.4-62.9% of the phenotypic variation across three years, respectively. Of which, 17 QTL with partially or completely overlapped confidence interval on chromosome A09, were homologous with two overlapped QTL on chromosome C08 by aligning QTL confidence intervals with the reference genomes of Brassica crops. By high density selective genotyping of DH lines with extreme phenotypes, using a Brassica single-nucleotide polymorphism (SNP) array, the QTL on chromosome A09 was narrowed, and aligned into 1.14-Mb region from 30.84 to 31.98 Mb on chromosome R09 of B. rapa and 1.05-Mb region from 27.21 to 28.26 Mb on chromosome A09 of B. napus. The alignment of QTL with Brassica reference genomes revealed homologous QTL on A09 and C08 for SL. The narrowed QTL region provides clues for gene cloning and breeding cultivars by marker-assisted selection.

Changes and roles of membrane compositions in the adaptation of Saccharomyces cerevisiae to ethanol

Bioethanol fermentation by Saccharomyces cerevisiae is often stressed by the accumulation of ethanol. Cell membrane is the first assaulting target of ethanol. Ethanol-adapted S. cerevisiae strains provide opportunity to shed light on membrane functions in the ethanol tolerance. This study aimed at clarifying the roles of cell membrane in the ethanol tolerance of S. cerevisiae through comparing membrane components between S. cerevisiae parental strain and ethanol-adapted strains. A directed evolutionary engineering was performed to obtain the ethanol-adapted S. cerevisiae strains. The parental, ethanol-adapted M5 and M10 strains were selected to be compared the percentage of viable cells after exposing to ethanol stress and cell membrane compositions (i.e., ergosterol, trehalose, and fatty acids). Compared with the parental strain, M5 or M10 strain had higher survival rate in the presence of 10% v/v ethanol. Compared with that in the parental strain, contents of trehalose, ergosterol, and fatty acids increased about 15.7, 12.1, and 29.3%, respectively, in M5 strain, and about 47.5, 107.8, and 61.5%, respectively, in M10 strain. Moreover, expression differences of genes involved in fatty acids metabolisms among the parental, M5 and M10 strains were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), and results demonstrated that M5 or M10 strain had higher expression of ACC1 and OLE1 than the parental strain. These results indicated that although being exposed to step-wise increased ethanol, S. cerevisiae cells might remodel membrane components or structure to adapt to the ethanol stress.

Auxotrophic Mutations Reduce Tolerance of Saccharomyces cerevisiae to Very High Levels of Ethanol Stress

Very high ethanol tolerance is a distinctive trait of the yeast Saccharomyces cerevisiae with notable ecological and industrial importance. Although many genes have been shown to be required for moderate ethanol tolerance (i.e., 6 to 12%) in laboratory strains, little is known of the much higher ethanol tolerance (i.e., 16 to 20%) in natural and industrial strains. We have analyzed the genetic basis of very high ethanol tolerance in a Brazilian bioethanol production strain by genetic mapping with laboratory strains containing artificially inserted oligonucleotide markers. The first locus contained the ura3Δ0 mutation of the laboratory strain as the causative mutation. Analysis of other auxotrophies also revealed significant linkage for LYS2, LEU2, HIS3, and MET15. Tolerance to only very high ethanol concentrations was reduced by auxotrophies, while the effect was reversed at lower concentrations. Evaluation of other stress conditions showed that the link with auxotrophy is dependent on the type of stress and the type of auxotrophy. When the concentration of the auxotrophic nutrient is close to that limiting growth, more stress factors can inhibit growth of an auxotrophic strain. We show that very high ethanol concentrations inhibit the uptake of leucine more than that of uracil, but the 500-fold-lower uracil uptake activity may explain the strong linkage between uracil auxotrophy and ethanol sensitivity compared to leucine auxotrophy. Since very high concentrations of ethanol inhibit the uptake of auxotrophic nutrients, the active uptake of scarce nutrients may be a major limiting factor for growth under conditions of ethanol stress.

Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression.

The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying gene expression variation is one approach to identify the mediating genes, however, it is difficult to distinguish causative from correlative genes. This becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation and only single static expression profiling is performed. As a proof of concept, we addressed this challenge here in yeast, by studying genome-wide gene expression in the presence of the causative polymorphism of MKT1 as the sole genetic variant, during the time phase when it contributes to sporulation efficiency variation. Our analysis during early sporulation identified mitochondrial retrograde signaling and nitrogen starvation as novel regulators, acting additively to regulate sporulation efficiency. Furthermore, we showed that PUF3, a known interactor of MKT1 had an independent role in sporulation. Our results highlight the role of differential mitochondrial signaling for efficient meiosis, providing insights into the factors regulating infertility. In addition, our study has implications for characterizing the molecular effects of causal genetic variants on dynamic biological processes during development and disease progression.

Chaotropicity: a key factor in product tolerance of biofuel-producing microorganisms

Fermentation products can chaotropically disorder macromolecular systems and induce oxidative stress, thus inhibiting biofuel production. Recently, the chaotropic activities of ethanol, butanol and vanillin have been quantified (5.93, 37.4, 174kJ kg(-1)m(-1) respectively). Use of low temperatures and/or stabilizing (kosmotropic) substances, and other approaches, can reduce, neutralize or circumvent product-chaotropicity. However, there may be limits to the alcohol concentrations that cells can tolerate; e.g. for ethanol tolerance in the most robust Saccharomyces cerevisiae strains, these are close to both the solubility limit (<25%, w/v ethanol) and the water-activity limit of the most xerotolerant strains (0.880). Nevertheless, knowledge-based strategies to mitigate or neutralize chaotropicity could lead to major improvements in rates of product formation and yields, and also therefore in the economics of biofuel production.

Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation

Background

Wine yeasts can produce undesirable sulfur compounds during alcoholic fermentation, such as SO₂ and H₂S, in variable amounts depending mostly on the yeast strain but also on the conditions. However, although sulfur metabolism has been widely studied, some of the genetic determinants of differences in sulfite and/or sulfide production between wine yeast strains remain to be identified. In this study, we used an integrated approach to decipher the genetic determinants of variation in the production of undesirable sulfur compounds.

Results

We examined the kinetics of SO₂ production by two parental strains, one high and one low sulfite producer. These strains displayed similar production profiles but only the high-sulfite producer strain continued to produce SO₂ in the stationary phase. Transcriptomic analysis revealed that the low-sulfite producer strain overexpressed genes of the sulfur assimilation pathway, which is the mark of a lower flux through the pathway consistent with a lower intracellular concentration in cysteine. A QTL mapping strategy then enabled us to identify MET2 and SKP2 as the genes responsible for these phenotypic differences between strains and we identified new variants of these genes in the low-sulfite producer strain. MET2 influences the availability of a metabolic intermediate, O-acetylhomoserine, whereas SKP2 affects the activity of a key enzyme of the sulfur assimilation branch of the pathway, the APS kinase, encoded by MET14. Furthermore, these genes also affected the production of propanol and acetaldehyde. These pleiotropic effects are probably linked to the influence of these genes on interconnected pathways and to the chemical reactivity of sulfite with other metabolites.

Conclusions

This study provides new insight into the regulation of sulfur metabolism in wine yeasts and identifies variants of MET2 and SKP2 genes, that control the activity of both branches of the sulfur amino acid synthesis pathway and modulate sulfite/sulfide production and other related phenotypes. These results provide novel targets for the improvement of wine yeast strains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-015-0245-1) contains supplementary material, which is available to authorized users.

A novel locus for mycelial aggregation forms a gateway to improved Streptomyces cell factories

BACKGROUND

Streptomycetes produce a plethora of natural products including antibiotics and anticancer drugs, as well as many industrial enzymes. Their mycelial life style is a major bottleneck for industrial exploitation and over decades strain improvement programs have selected production strains with better growth properties. Uncovering the nature of the underlying mutations should allow the ready transfer of desirable traits to other production hosts.

RESULTS

Here we report that the mat gene cluster, which was identified through reverse engineering of a non-pelleting mutant selected in a chemostat, is key to pellet formation of Streptomyces lividans. Deletion of matA or matB, which encode putative polysaccharide synthases, effects mycelial metamorphosis, with very small and open mycelia. Growth rate and productivity of the matAB null mutant were increased by over 60% as compared to the wild-type strain.

CONCLUSION

Here, we present a way to counteract pellet formation by streptomycetes, which is one of the major bottlenecks in their industrial application. The mat locus is an ideal target for rational strain design approaches aimed at improving streptomycetes as industrial production hosts.

Protocols and programs for high-throughput growth and aging phenotyping in yeast

In microorganisms, and more particularly in yeasts, a standard phenotyping approach consists in the analysis of fitness by growth rate determination in different conditions. One growth assay that combines high throughput with high resolution involves the generation of growth curves from 96-well plate microcultivations in thermostated and shaking plate readers. To push the throughput of this method to the next level, we have adapted it in this study to the use of 384-well plates. The values of the extracted growth parameters (lag time, doubling time and yield of biomass) correlated well between experiments carried out in 384-well plates as compared to 96-well plates or batch cultures, validating the higher-throughput approach for phenotypic screens. The method is not restricted to the use of the budding yeast Saccharomyces cerevisiae, as shown by consistent results for other species selected from the Hemiascomycete class. Furthermore, we used the 384-well plate microcultivations to develop and validate a higher-throughput assay for yeast Chronological Life Span (CLS), a parameter that is still commonly determined by a cumbersome method based on counting "Colony Forming Units". To accelerate analysis of the large datasets generated by the described growth and aging assays, we developed the freely available software tools GATHODE and CATHODE. These tools allow for semi-automatic determination of growth parameters and CLS behavior from typical plate reader output files. The described protocols and programs will increase the time- and cost-efficiency of a number of yeast-based systems genetics experiments as well as various types of screens.

Genetic mapping of the nulliplex-branch gene (gb_nb1) in cotton using next-generation sequencing

Using bulked segregant analysis based on next-generation sequencing, the recessive nulliplex-branch gene was mapped between two SNP markers ~600 kb apart. In a "nulliplex-branch" cotton mutant, most of the flowers arise directly from leaf axils on the main shoot, which usually does not have a fruiting branch. A nulliplex-branch is a useful trait by which to study cotton architecture; however, the genetic basis of this mutant has remained elusive. In this study, bulked segregant analysis combined with next-generation sequencing technology was used to finely map the underlying genes that result in a nulliplex-branch plant. The nulliplex-branch Pima cotton variety, Xinhai-18, was crossed with the normal branch upland cotton line, TM-1, resulting in an F2 population. The nulliplex-branch trait was found to be controlled by the recessive gene gb_nb1. Allelic single-nucleotide polymorphisms (SNPs) were discovered by reduced-representation sequencing between the parents, and their profiles were also characterized in the nulliplex-branch and normal branch bulks constructed using the F2 plants. A candidate ~9.0 Mb-long region comprising 42 SNP markers was found to be associated with gb_nb1, which helped localize it at the ~600-kb interval on Chr 16 by segregation analysis in the F2 population. The closely linked markers with gb_nb1 developed in this study will facilitate the marker-assisted selection of the nulliplex-branch trait, and the fine map constructed will accelerate map-based cloning of gb_nb1.

Large-scale robot-assisted genome shuffling yields industrial Saccharomyces cerevisiae yeasts with increased ethanol tolerance

BACKGROUND

During the final phases of bioethanol fermentation, yeast cells face high ethanol concentrations. This stress results in slower or arrested fermentations and limits ethanol production. Novel Saccharomyces cerevisiae strains with superior ethanol tolerance may therefore allow increased yield and efficiency. Genome shuffling has emerged as a powerful approach to rapidly enhance complex traits including ethanol tolerance, yet previous efforts have mostly relied on a mutagenized pool of a single strain, which can potentially limit the effectiveness. Here, we explore novel robot-assisted strategies that allow to shuffle the genomes of multiple parental yeasts on an unprecedented scale.

RESULTS

Screening of 318 different yeasts for ethanol accumulation, sporulation efficiency, and genetic relatedness yielded eight heterothallic strains that served as parents for genome shuffling. In a first approach, the parental strains were subjected to multiple consecutive rounds of random genome shuffling with different selection methods, yielding several hybrids that showed increased ethanol tolerance. Interestingly, on average, hybrids from the first generation (F1) showed higher ethanol production than hybrids from the third generation (F3). In a second approach, we applied several successive rounds of robot-assisted targeted genome shuffling, yielding more than 3,000 targeted crosses. Hybrids selected for ethanol tolerance showed increased ethanol tolerance and production as compared to unselected hybrids, and F1 hybrids were on average superior to F3 hybrids. In total, 135 individual F1 and F3 hybrids were tested in small-scale very high gravity fermentations. Eight hybrids demonstrated superior fermentation performance over the commercial biofuel strain Ethanol Red, showing a 2 to 7% increase in maximal ethanol accumulation. In an 8-l pilot-scale test, the best-performing hybrid fermented medium containing 32% (w/v) glucose to dryness, yielding 18.7% (v/v) ethanol with a productivity of 0.90 g ethanol/l/h and a yield of 0.45 g ethanol/g glucose.

CONCLUSIONS

We report the use of several different large-scale genome shuffling strategies to obtain novel hybrids with increased ethanol tolerance and fermentation capacity. Several of the novel hybrids show best-parent heterosis and outperform the commonly used bioethanol strain Ethanol Red, making them interesting candidate strains for industrial production.

A nonhomogeneous hidden markov model for gene mapping based on next-generation sequencing data

The analysis of polygenetic characteristics for mapping quantitative trait loci (QTL) remains an important challenge. QTL analysis requires two or more strains of organisms that differ substantially in the (poly-)genetic trait of interest, resulting in a heterozygous offspring. The offspring with the trait of interest is selected and subsequently screened for molecular markers such as single-nucleotide polymorphisms (SNPs) with next-generation sequencing. Gene mapping relies on the co-segregation between genes and/or markers. Genes and/or markers that are linked to a QTL influencing the trait will segregate more frequently with this locus. For each identified marker, observed mismatch frequencies between the reads of the offspring and the parental reference strains can be modeled by a multinomial distribution with the probabilities depending on the state of an underlying, unobserved Markov process. The states indicate whether the SNP is located in a (vicinity of a) QTL or not. Consequently, genomic loci associated with the QTL can be discovered by analyzing hidden states along the genome. The aforementioned hidden Markov model assumes that the identified SNPs are equally distributed along the chromosome and does not take the distance between neighboring SNPs into account. The distance between the neighboring SNPs could influence the chance of co-segregation between genes and markers. To address this issue, we propose a nonhomogeneous hidden Markov model with a transition matrix that depends on a set of distance-varying observed covariates. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast.

Genetic mapping of MAPK-mediated complex traits Across S. cerevisiae

Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we identified QTL that influence phenotypes critically dependent on MAPK signaling cascades. Genetic variants under these QTL fall within MAPK signaling networks themselves as well as other interconnected signaling pathways. Finally, we demonstrate how the mapping results from multiple strain background can be leveraged to narrow the search space of causal genetic variants.

Wild yeast strains differ in phenotypes that are controlled by highly conserved signaling pathways. Yet it remains unknown how naturally occurring genetic variants influence signaling pathways in yeast. We have developed an approach to facilitate the mapping of genetic variants that underlie these phenotypic differences in a set of wild strain. Our mapping approach requires minimal strain engineering and enables the rapid isolation of mapping populations from any strain background. In particular, we have mapped genetic variants in twelve highly diverse yeast strains. Further, we demonstrate how the mapping results from these twelve strains can be used jointly to narrow the number of genetic variants identified to a set of putative causal variants. We identify genetic variants in genes with various roles in cell signaling. Our results illustrate the interplay of different signaling pathways and which signaling genes are more likely to contain variants of large phenotypic impact.

Tiled ChrI RHS collection: a pilot high-throughput screening tool for identification of allelic variants

Reciprocal hemizygosity analysis is a genetic technique that allows phenotypic determination of the allelic effects of a gene in a genetically uniform background. Expanding this single gene technique to generate a genome-wide collection is termed as reciprocal hemizygosity scanning (RHS). The RHS collection should circumvent the need for linkage mapping and provide the power to identify all possible allelic variants for a given phenotype. However, the published RHS collections based on the existing genome-wide haploid deletion library reported a high rate of false positives. In this study, we report de novo construction of a RHS collection that is not based on the yeast deletion library. This collection has been constructed for the shortest yeast chromosome, ChrI. Using this ChrI RHS collection, we identified 13 allelic variants for the previously mapped loci and novel allelic variants for the growth differences in different environments. A few of these novel variants, which were fine mapped to a gene level, identified novel genetic variation for the previously studied environmental conditions. The availability of a genome-wide RHS collection would thus help us uncover a comprehensive list of allelic variants and better our understanding of the molecular pathways modulating a quantitative trait.