Ethanol Tolerance Of Yeast Research Articles

The conversion of biomass to fuels via cutting-edge technologies: Explorations from natural utilization systems

Biomass can be used to manufacture both hydrocarbon fuels and chemical compounds such as rubber, alcohols, lipid-based products, sugars, etc. Due to their abundant supply and lower greenhouse gas emissions, fuels derived from biomass have received much attention in recent years. Biofuels can be made from grasses, agricultural waste, animal waste and trash, old oils, and other organic things. The characteristics of various feedstocks and fuels, as well as the techniques for converting biomass to biofuels, physical and chemical parameters, and limitations that affect biomass to fuel conversion, are all discussed. pH, temperature, and residence time are all process parameters. Other chemical factors include the availability of carbon, nutrients, acid and alkaline hydrolysis agents, phenolic inhibitors, and sugars produced throughout the process. The list of obstacles to biomass bioconversion includes reduced particle size, crystallinity, fermentation inhibitory byproducts, cellulase deactivation, yeast ethanol tolerance, and sugar co-fermentation. The research and development of biomass-to-energy conversion technology have been accelerating significantly, according to this review. The choice of the conversion method depends on the type and quality of the biomass, the final energy form, environmental requirements, economic conditions, and project-specific considerations. Biowaste is initially improved (depending on the conditions). This process includes size reduction, densification, drying, and torrefaction. The two most popular conversion methods for biomass to energy are thermochemical and biochemical. Utilizing the power of microorganisms, biochemical conversion transforms biomass into liquid or gaseous fuels. The processes of anaerobic digestion, fermentation, and photobiological hydrogen generation are currently the most widely used biochemical technologies. Recent advancements in recombinant DNA technology and protein engineering, as well as future technology like extraction, and application towards green, are targeted at addressing bioconversion restrictions.

The multiple roles of lipid metabolism in yeast physiology during beer fermentation.

The ability of brewing yeasts (Saccharomyces cerevisiae and Saccharomyces pastorianus) to cope with the toxic effects of ethanol during beer fermentation depends on the modulation of lipid and lipid droplets (LDs) biosynthesis, which affects membrane fluidity. However, it has been demonstrated that lipids and LDs can modulate different biological mechanisms associated to ethanol tolerance, including proteostasis and autophagy, leading to the hypothesis that lipid and LDs biosynthesis are integrative processes necessary for ethanol tolerance in yeast. Supporting this hypothesis, a transcriptome and systems biology analyses indicated the upregulation of autophagy, lipid biosynthesis, and proteostasis (ALP)-associated genes in lager yeast during beer fermentation, whose respective proteins interact in a shortest-pathway ALP network. These results indicated a cross-communication between various pathways linked to inter-organelle autophagy, lipid metabolism, and proteostasis (ALP) during lager beer fermentation, thus highlighting the importance of lipids for beer fermentation.

Integrated Multi-Omics Analysis of Mechanisms Underlying Yeast Ethanol Tolerance.

For yeast cells, tolerance to high levels of ethanol is vital both in their natural environment and in industrially relevant conditions. We recently genotyped experimentally evolved yeast strains adapted to high levels of ethanol and identified mutations linked to ethanol tolerance. In this study, by integrating genomic sequencing data with quantitative proteomics profiles from six evolved strains (data set identifier PXD006631) and construction of protein interaction networks, we elucidate exactly how the genotype and phenotype are related at the molecular level. Our multi-omics approach points to the rewiring of numerous metabolic pathways affected by genomic and proteomic level changes, from energy-producing and lipid pathways to differential regulation of transposons and proteins involved in cell cycle progression. One of the key differences is found in the energy-producing metabolism, where the ancestral yeast strain responds to ethanol by switching to respiration and employing the mitochondrial electron transport chain. In contrast, the ethanol-adapted strains appear to have returned back to energy production mainly via glycolysis and ethanol fermentation, as supported by genomic and proteomic level changes. This work is relevant for synthetic biology where systems need to function under stressful conditions, as well as for industry and in cancer biology, where it is important to understand how the genotype relates to the phenotype.

Effects of Lactobacillus plantarum on the ethanol tolerance of Saccharomyces cerevisiae

The bioethanol fermentation by Saccharomyces cerevisiae is often challenged by bacterial contamination, especially lactic acid bacteria (LAB). LAB can inhibit the growth S. cerevisiae by secreting organic acids and competing for nutrients and physical space. However, the range of favorable effects attributed to LAB during bioethanol fermentation, and their associated mechanisms of regulation, are not fully understood. This study was performed to clarify the effects of Lactobacillus plantarum, an important contaminative LAB in bioethanol fermentation, on the mechanism of ethanol tolerance in S. cerevisiae. The results showed that the presence of L. plantarum increased the ethanol tolerance of S. cerevisiae by promoting or inhibiting various metabolic processes in the yeast cells: The metabolism of trehalose, ergosterol, certain amino acids, proton pumps, stress response transcriptional activators, and heat shock proteins were all promoted; amounts of intracellular monounsaturated fatty acids and the accumulation of reactive oxygen species were inhibited. Furthermore, the maintenance of the acquired higher ethanol tolerance of S. cerevisiae was dependent on the coexistence of L. plantarum. These results suggested a complex relationship existed between S. cerevisiae and the contaminating LAB that might also play a beneficial role during fermentation by promoting the ethanol tolerance of yeast. The results from this study suggested that the extent of controlling bacterial contamination on bioethanol fermentation efficiency should be given careful consideration. KEY POINTS: • L. plantarum improved the ethanol tolerance of S. cerevisiae. • L. plantarum regulated the ethanol tolerance-related metabolism of yeast cells. • L. plantarum coexistence facilitated maintenance of ethanol tolerance in yeast cells.

Enhanced mutation through exposure to EMS affects the evolution of ethanol tolerance in Saccharomyces cerevisiae

Ethanol is a multifunctional compound that has many uses and can be made naturally by sugar-fermenting yeast such as Saccharomyces cerevisiae. However, ethanol is toxic to yeast, so in this study we aimed to improve yeast ethanol tolerance, which could result in greater efficiencies in the process of alcohol production. This research explores the effectiveness of artificially inducing population-level variation through the application of ethyl methanesulfonate (EMS) on increasing the ethanol tolerance in S. cerevisiae. After several rounds of selection and increasing ethanol concentrations, ranging from 9-27%, the two treatment populations (AS - Artificial Selection only; and EMS – Exposure to EMS and Artificial Selection) were compared to the initial parental population. The parental strain was tolerant to ethanol concentrations of 13%, while the AS treatment was tolerant to slightly higher levels, 16%. Both of these treatments were well below the EMS treatment which expressed a tolerance of 27%. To test differences between strains, the parental strain, selection strain, and the EMS-exposed strain were separately plated on ten 27% ethanol plates and ten 0% ethanol plates, and growth was checked after 24 hours. The EMS-exposed strain was the only strain that grew at 27% ethanol; all strains grew at 0% ethanol. These results show that EMS, artificial selection, and ethanol as a stressor might be effective in producing strains of S. cerevisiae that are able to produce greater amounts of ethanol before toxicity sets in.

Mitophagy Improves Ethanol Tolerance in Yeast: Regulation by Mitochondrial Reactive Oxygen Species in Saccharomyces cerevisiae.

Ethanol often accumulates during the process of wine fermentation, and mitophagy has critical role in ethanol output. However, the relationship between mitophagy and ethanol stress is still unclear. In this study, the expression of ATG11 and ATG32 genes exposed to ethanol stress was accessed by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). The result indicated that ethanol stress induced expression of the ATG11 and ATG32 genes. The colony sizes and the alcohol yield of atg11 and atg32 were also smaller and lower than those of wild type strain under ethanol whereas the mortality of mutants is higher. Furthermore, compared with wild type, the membrane integrity and the mitochondrial membrane potential of atg11 and atg32 exhibited greater damage following ethanol stress. In addition, a greater proportion of mutant cells were arrested at the G1/G0 cell cycle. There was more aggregation of peroxide hydrogen (H2O2) and superoxide anion (O2•-) in mutants. These changes in H2O2 and O2•- in yeasts were altered by reductants or inhibitors of scavenging enzyme by means of regulating the expression of ATG11 and ATG32 genes. Inhibitors of the mitochondrial electron transport chain (mtETC) also increased production of H2O2 and O2•- by enhancing expression of the ATG11 and ATG32 genes. Further results showed that activator or inhibitor of autophagy also activated or inhibited mitophagy by altering production of H2O2 and O2•. Therefore, ethanol stress induces mitophagy which improves yeast the tolerance to ethanol and the level of mitophagy during ethanol stress is regulated by ROS derived from mtETC.

Factors affecting yeast ethanol tolerance and fermentation efficiency.

Alcohol fermentation is a key process in wine, beer, alcoholic beverage production, bioethanol production by means of carbohydrate sources, and food industry byproducts. There are three key points in these kinds of processes determining their efficiency; enzymatic cellulose lysis into simple sugar molecules, alcohol fermentation rate, and ethanol tolerance of yeast cells. The first process is usually carried out by either the use of pure cellulolytic enzymes, which is a high cost procedure, or by the production of these enzymes from cellulolytic bacteria and filamentous fungi. Lately, Saccharomyces cerevisiae and several other yeasts were genetically modified to express recombinant cellulases in media or display them on the cell surface. Many studies have indicated that the genetic engineering of yeast cells can be a useful approach in increasing the alcoholic fermentation rate as well as their ethanol tolerance. These modifications could be the overexpression of a key protein using a strong promoter or the modification of a specific domain or amino acid which can also lead to the desired outcome. This review focuses on the modifications of a single protein and/or pathways that can lead to the augmentation of ethanol tolerance and alcoholic fermentation efficiency of Saccharomyces cerevisiae.

Membrane Fluidity of Saccharomyces cerevisiae from Huangjiu (Chinese Rice Wine) Is Variably Regulated by OLE1 To Offset the Disruptive Effect of Ethanol.

An evolution and resequencing strategy was used to research the genetic basis of Saccharomyces cerevisiae BR20 (with 18 vol% ethanol tolerance) and the evolved strain F23 (with 25 vol% ethanol tolerance). Whole-genome sequencing and RNA sequencing (RNA-seq) indicated that the enhanced ethanol tolerance under 10 vol% ethanol could be attributed to amino acid metabolism, whereas 18 vol% ethanol tolerance was due to fatty acid metabolism. Ultrastructural analysis indicated that F23 exhibited better membrane integrity than did BR20 under ethanol stress. At low concentrations (<5 vol%), the partition of ethanol into the membrane increased the membrane fluidity, which had little effect on cell growth. However, the toxic effects of medium and high ethanol concentrations (5 to 20 vol%) tended to decrease the membrane fluidity. Under high ethanol stress (>10 vol%), the highly tolerant strain was able to maintain a relatively constant fluidity by increasing the content of unsaturated fatty acid (UFA), whereas less-tolerant strains show a continuous decrease in fluidity and UFA content. OLE1, which was identified as the only gene with a differential single-nucleotide polymorphism (SNP) mutation site related to fatty acid metabolism, was significantly changed in response to ethanol. The role of OLE1 in membrane fluidity was positively validated in its overexpressed transformants. Therefore, OLE1 lowered the rate of decline in membrane fluidity and thus enabled the yeast to better fight the deleterious effects of ethanol.IMPORTANCE Yeasts with superior ethanol tolerance are desirable for winemakers and wine industries. In our previous work, strain F23 was evolved with superior ethanol tolerance and fermentation activity to improve the flavor profiles of Chinese rice wine. Therefore, exploring the genomic variations and ethanol tolerance mechanism of strain F23 could contribute to an understanding of its effect on the flavor characteristics in the resulting Chinese rice wine. The cellular membrane plays a vital role in the ethanol tolerance of yeasts; however, how the membrane is regulated to fight the toxic effect of ethanol remains to be elucidated. This study suggests that the membrane fluidity is variably regulated by OLE1 to offset the disruptive effect of ethanol. Current work will help develop more ethanol-tolerant yeast strains for wine industries and contribute to a deep understanding of its high flavor-producing ability.

FLO Genes Family and Transcription Factor MIG1 Regulate Saccharomyces cerevisiae Biofilm Formation During Immobilized Fermentation.

Saccharomyces cerevisiae immobilization is commonly used for efficient ethanol fuel production in industry due to the relatively higher ethanol stress resistance of S. cerevisiae in biofilms relative to planktonic cells. The mechanisms of biofilm formation and stress resistance, however, remain ambiguous. By analyzing biofilm and planktonic cell transcriptomes, this study observed that MIG1 (encoding a transcription factor) expression in cells increases during the biofilm formation process. To identify the role of MIG1 in yeast biofilm formation and the ethanol resistance of these cells, MIG1 was deleted and complemented in S. cerevisiae 1308. Results showed the MIG1 deletion mutant strain demonstrated weaker biofilm formation ability both on fibers and plastic than the wild-type and these could be restored by expressing MIG1 in deletion mutant. To verify the ability of MIG1 to regulate the expression of FLO genes, which encode adhesions responsible for yeast biofilm formation, FLO gene transcription levels were measured via qRT-PCR. Relative to wild-type S. cerevisiae, the adhesion genes FLO1, 5, and 9 which also demonstrate increased expression in the transcriptome of yeast cells during biofilm formation, but not FLO11, were down-regulated in the MIG1 mutant strain. Additionally, the MIG1 mutant lost a majority of its flocculation ability, which depended on cell-cell adhesions and its slightly invasive growth ability, dependent on cell-substrate adhesion. Deleting FLO1, 5, and 9 decreased biofilm formation on plastics, suggesting these FLO genes contribute to the biofilm formation process alongside FLO11. Moreover, the ethanol tolerance of yeast decreased in the MIG1 deletion mutant as well as the FLO11 deletion mutant, resulting in reduced biofilm formation during fermentation. It remains possible that in the later period of fermentation, when ethanol has accumulated, an over-expression of the FLO1, 5, and 9 genes regulated by MIG1 would enhanced cell-cell adhesions and thus protect cells in the outer layer of biofilms from ethanol, a function primarily dependent on cell-cell adhesions. This work offers a possible explanation for how biofilm formation is regulated during the immobilized fermentation process, and can enhance environmental tolerance in industrial production.

Industrial Applications of Selected JFS Articles

Industrial Applications of Selected <i>JFS</i> Articles

Improving Saccharomyces cerevisiae ethanol production and tolerance via RNA polymerase II subunit Rpb7

BackgroundClassical strain engineering methods often have limitations in altering multigenetic cellular phenotypes. Here we try to improve Saccharomyces cerevisiae ethanol tolerance and productivity by reprogramming its transcription profile through rewiring its key transcription component RNA polymerase II (RNAP II), which plays a central role in synthesizing mRNAs. This is the first report on using directed evolution method to engineer RNAP II to alter S. cerevisiae strain phenotypes.ResultsError-prone PCR was employed to engineer the subunit Rpb7 of RNAP II to improve yeast ethanol tolerance and production. Based on previous studies and the presumption that improved ethanol resistance would lead to enhanced ethanol production, we first isolated variant M1 with much improved resistance towards 8 and 10% ethanol. The ethanol titers of M1 was ~122 g/L (96.58% of the theoretical yield) under laboratory very high gravity (VHG) fermentation, 40% increase as compared to the control. DNA microarray assay showed that 369 genes had differential expression in M1 after 12 h VHG fermentation, which are involved in glycolysis, alcoholic fermentation, oxidative stress response, etc.ConclusionsThis is the first study to demonstrate the possibility of engineering eukaryotic RNAP to alter global transcription profile and improve strain phenotypes. Targeting subunit Rpb7 of RNAP II was able to bring differential expression in hundreds of genes in S. cerevisiae, which finally led to improvement in yeast ethanol tolerance and production.

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

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.

유전자 상호발현 조절을 통한 에탄올 내성 메커니즘의 규명

Ethanol is a very valuable material, however, it is also a source of stress, as the accumulation of etha-nol in a medium inhibits cell viability and decreases productivity of the target product. Therefore, the ethanol tolerance of yeast, which is closely related to ethanol productivity, is an important factor in industrial ethanol production. In this study, the YDJ1 and PEP5 genes were selected as target genes for elucidating ethanol-tolerant mechanisms by analyzing the expression regulation of these genes. The pA-YDJ1 and pA-PEP5 plasmids containing YDJ1 and PEP5 genes under an ADH1 promoter, re-spectively, were constructed and transformed into BY4742 (host strain), BY4742△ydj1, and BY4742△pep5 strains. The ethanol tolerance in the BY4742△ydj1/ pA-YDJ1 and BY4742△pep5/pA-PEP5 trans-formants was restored by overexpression of the YDJ1 and PEP5 genes to the host strain level. The YDJ1 and PEP5 genes were also introduced into the double gene disruptant (BY4742△ydj1△pep5) to investigate the expression regulation of the YDJ1 and PEP5 genes. The simultaneous overexpression of the YDJ1 and PEP5 genes restored ethanol tolerance to the 90% level of the BY4742 strain under 8% ethanol stress. The YDJ1 gene induced more overexpression of the PEP5 gene in the BY4742△ydj1△pep5/pA-YDJ1, pA-PEP5 strain, suggesting that the YDJ1 gene partially regulates the expression of the PEP5 gene as an upstream regulator.

TALENs-Assisted Multiplex Editing for Accelerated Genome Evolution To Improve Yeast Phenotypes.

Genome editing is an important tool for building novel genotypes with a desired phenotype. However, the fundamental challenge is to rapidly generate desired alterations on a genome-wide scale. Here, we report TALENs (transcription activator-like effector nucleases)-assisted multiplex editing (TAME), based on the interaction of designed TALENs with the DNA sequences between the critical TATA and GC boxes, for generating multiple targeted genomic modifications. Through iterative cycles of TAME to induce abundant semirational indels coupled with efficient screening using a reporter, the targeted fluorescent trait can be continuously and rapidly improved by accumulating multiplex beneficial genetic modifications in the evolving yeast genome. To further evaluate its efficiency, we also demonstrate the application of TAME for significantly improving ethanol tolerance of yeast in a short amount of time. Therefore, TAME is a broadly generalizable platform for accelerated genome evolution to rapidly improve yeast phenotypes.

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.

A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data.

The analysis of polygenic, phenotypic characteristics such as quantitative traits or inheritable diseases 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 as genetic markers. Combining the technologies with pooling of segregants, as performed in bulk segregant analysis, should, in principle, allow the simultaneous mapping of multiple genetic loci present throughout the genome. We propose a hidden Markov-model to analyze the marker data obtained by the bulk segregant next generation sequencing. The model includes several states, each associated with a different probability of observing the same/different nucleotide in an offspring as compared to the parent. The transitions between the molecular markers imply transitions between the states of the model. After estimating the transition probabilities and state-related probabilities of nucleotide (dis)similarity, the most probable state for each SNP is selected. The most probable states can then be used to indicate which genomic regions may be likely to contain trait-related genes. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast. Software is written in R. R-functions, R-scripts and documentation are available on www.ibiostat.be/software/bioinformatics.

A system based network approach to ethanol tolerance in Saccharomyces cerevisiae.

BackgroundSaccharomyces cerevisiae has been widely used for bio-ethanol production and development of rational genetic engineering strategies leading both to the improvement of productivity and ethanol tolerance is very important for cost-effective bio-ethanol production. Studies on the identification of the genes that are up- or down-regulated in the presence of ethanol indicated that the genes may be involved to protect the cells against ethanol stress, but not necessarily required for ethanol tolerance.ResultsIn the present study, a novel network based approach was developed to identify candidate genes involved in ethanol tolerance. Protein-protein interaction (PPI) network associated with ethanol tolerance (tETN) was reconstructed by integrating PPI data with Gene Ontology (GO) terms. Modular analysis of the constructed networks revealed genes with no previously reported experimental evidence related to ethanol tolerance and resulted in the identification of 17 genes with previously unknown biological functions. We have randomly selected four of these genes and deletion strains of two genes (YDR307W and YHL042W) were found to exhibit improved tolerance to ethanol when compared to wild type strain.The genome-wide transcriptomic response of yeast cells to the deletions of YDR307W and YHL042W in the absence of ethanol revealed that the deletion of YDR307W and YHL042W genes resulted in the transcriptional re-programming of the metabolism resulting from a mis-perception of the nutritional environment. Yeast cells perceived an excess amount of glucose and a deficiency of methionine or sulfur in the absence of YDR307W and YHL042W, respectively, possibly resulting from a defect in the nutritional sensing and signaling or transport mechanisms. Mutations leading to an increase in ribosome biogenesis were found to be important for the improvement of ethanol tolerance. Modulations of chronological life span were also identified to contribute to ethanol tolerance in yeast.ConclusionsThe system based network approach developed allows the identification of novel gene targets for improved ethanol tolerance and supports the highly complex nature of ethanol tolerance in yeast.

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

BackgroundBulk 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.ResultsTo 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.ConclusionsEXPLoRA 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.

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.

Increased expression of the yeast multidrug resistance ABC transporter Pdr18 leads to increased ethanol tolerance and ethanol production in high gravity alcoholic fermentation

BackgroundThe understanding of the molecular basis of yeast tolerance to ethanol may guide the design of rational strategies to increase process performance in industrial alcoholic fermentations. A set of 21 genes encoding multidrug transporters from the ATP-Binding Cassette (ABC) Superfamily and Major Facilitator Superfamily (MFS) in S. cerevisiae were scrutinized for a role in ethanol stress resistance.ResultsA yeast multidrug resistance ABC transporter encoded by the PDR18 gene, proposed to play a role in the incorporation of ergosterol in the yeast plasma membrane, was found to confer resistance to growth inhibitory concentrations of ethanol. PDR18 expression was seen to contribute to decreased 3 H-ethanol intracellular concentrations and decreased plasma membrane permeabilization of yeast cells challenged with inhibitory ethanol concentrations. Given the increased tolerance to ethanol of cells expressing PDR18, the final concentration of ethanol produced during high gravity alcoholic fermentation by yeast cells devoid of PDR18 was lower than the final ethanol concentration produced by the corresponding parental strain. Moreover, an engineered yeast strain in which the PDR18 promoter was replaced in the genome by the stronger PDR5 promoter, leading to increased PDR18 mRNA levels during alcoholic fermentation, was able to attain a 6 % higher ethanol concentration and a 17 % higher ethanol production yield than the parental strain. The improved fermentative performance of yeast cells over-expressing PDR18 was found to correlate with their increased ethanol tolerance and ability to restrain plasma membrane permeabilization induced throughout high gravity fermentation.ConclusionsPDR18 gene over-expression increases yeast ethanol tolerance and fermentation performance leading to the production of highly inhibitory concentrations of ethanol. PDR18 overexpression in industrial yeast strains appears to be a promising approach to improve alcoholic fermentation performance for sustainable bio-ethanol production.