High ethanol tolerance is an exquisite characteristic of the yeast Saccharomyces cerevisiae, which enables this microorganism to dominate in natural and industrial fermentations. Up to now, ethanol tolerance has only been analyzed in laboratory yeast strains with moderate ethanol tolerance. The genetic basis of the much higher ethanol tolerance in natural and industrial yeast strains is unknown. We have applied pooled-segregant whole-genome sequence analysis to map all quantitative trait loci (QTL) determining high ethanol tolerance. We crossed a highly ethanol-tolerant segregant of a Brazilian bioethanol production strain with a laboratory strain with moderate ethanol tolerance. Out of 5974 segregants, we pooled 136 segregants tolerant to at least 16% ethanol and 31 segregants tolerant to at least 17%. Scoring of SNPs using whole-genome sequence analysis of DNA from the two pools and parents revealed three major loci and additional minor loci. The latter were more pronounced or only present in the 17% pool compared to the 16% pool. In the locus with the strongest linkage, we identified three closely located genes affecting ethanol tolerance: MKT1, SWS2, and APJ1, with SWS2 being a negative allele located in between two positive alleles. SWS2 and APJ1 probably contained significant polymorphisms only outside the ORF, and lower expression of APJ1 may be linked to higher ethanol tolerance. This work has identified the first causative genes involved in high ethanol tolerance of yeast. It also reveals the strong potential of pooled-segregant sequence analysis using relatively small numbers of selected segregants for identifying QTL on a genome-wide scale.

Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast.

Recent advances in high-throughput sequencing (HTS) technologies and computing capacity have produced unprecedented amounts of genomic data that have unraveled the genetics of phenotypic variability in several species. However, operating and integrating current software tools for data analysis still require important investments in highly skilled personnel. Developing accurate, efficient and user-friendly software packages for HTS data analysis will lead to a more rapid discovery of genomic elements relevant to medical, agricultural and industrial applications. We therefore developed Next-Generation Sequencing Eclipse Plug-in (NGSEP), a new software tool for integrated, efficient and user-friendly detection of single nucleotide variants (SNVs), indels and copy number variants (CNVs). NGSEP includes modules for read alignment, sorting, merging, functional annotation of variants, filtering and quality statistics. Analysis of sequencing experiments in yeast, rice and human samples shows that NGSEP has superior accuracy and efficiency, compared with currently available packages for variants detection. We also show that only a comprehensive and accurate identification of repeat regions and CNVs allows researchers to properly separate SNVs from differences between copies of repeat elements. We expect that NGSEP will become a strong support tool to empower the analysis of sequencing data in a wide range of research projects on different species.

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.

Gpd1 and Gpd2 are the two isoforms of glycerol 3-phosphate dehydrogenase (GPDH), which is the ratecontrolling enzyme of glycerol formation in Saccharomyces cerevisiae. The two isoenzymes play crucial roles in osmoregulation and redox balancing. Past approaches to increase ethanol yield at the cost of reduced glycerol yield have most often been based on deletion of either one or two isogenes (GPD1 and GPD2). While single deletions of GPD1 or GPD2 reduced glycerol formation only slightly, the gpd1⌬ gpd2⌬ double deletion strain produced zero glycerol but showed an osmosensitive phenotype and abolished anaerobic growth. Our current approach has sought to generate "intermediate" phenotypes by reducing both isoenzyme activities without abolishing them. To this end, the GPD1 promoter was replaced in a gpd2⌬ background by two lower-strength TEF1 promoter mutants. In the same manner, the activity of the GPD2 promoter was reduced in a gpd1⌬ background. The resulting strains were crossed to obtain different combinations of residual GPD1 and GPD2 expression levels. Among our engineered strains we identified four candidates showing improved ethanol yields compared to the wild type. In contrast to a gpd1⌬ gpd2⌬ double-deletion strain, these strains were able to completely ferment the sugars under quasi-anaerobic conditions in both minimal medium and during simultaneous saccharification and fermentation (SSF) of liquefied wheat mash (wheat liquefact). This result implies that our strains can tolerate the ethanol concentration at the end of the wheat liquefact SSF (up to 90 g liter ؊1 ). Moreover, a few of these strains showed no significant reduction in osmotic stress tolerance compared to the wild type.

Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle methodspecific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.I n the last decades, a nutritious diet low in calories, commonly referred to as calorie restriction (CR), has been reported to extend the lifespan of a broad range of organisms, such as yeast, worms, fruit flies, mice, rats, and monkeys (1-6). This seemingly evolutionary conserved effect of CR on aging has sparked intense investigation into its underlying molecular mechanisms, especially in the budding yeast Saccharomyces cerevisiae (7-10). Some studies suggest that nutrient-responsive pathways, such as target of rapamycin (TOR), protein kinase A (PKA), and Sch9 (11), mediate CR-induced lifespan extension, whereas others hypothesize that CR increases the activity of sirtuin deacetylases (e.g., SIR2) extending lifespan by suppressing ribosomal DNA recombination (9, 12, 13).In S. cerevisiae, lifespan can either be defined by the time cells remain viable in absence of nutrients, called chronological lifespan, or by the number of daughter cells a yeast cell produces during its life, which is referred to as replicative lifespan (RLS) (14). Although RLS can vary greatly between individual yeast cells, the average RLS (i.e., across a population of cells) is assumed to be a genotypic trait (15-19). However, besides CR, other environmental conditions, such as pH and carbon source, are known to have a strong impact on RLS (20, 21).Here, through a systematic analysis of RLS data of S. cerevisiae using data from more than 27,000 cells, we obtained indication that small sample sizes might be the cause for the often observed significant variation between RLS data. Further, we identified that partly biased data exis...

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

A comprehensive description of the phenotypic changes during cellular aging is key towards unraveling its causal forces. Previously, we mapped age-related changes in the proteome and transcriptome (Janssens et al., 2015). Here, employing the same experimental procedure and model-based inference, we generate a comprehensive account of metabolic changes during the replicative life of Saccharomyces cerevisiae. With age, we found decreasing metabolite levels, decreasing growth and substrate uptake rates accompanied by a switch from aerobic fermentation to respiration, with glycerol and acetate production. The identified metabolic fluxes revealed an increase in redox cofactor turnover, likely to combat increased production of reactive oxygen species. The metabolic changes are possibly a result of the age-associated decrease in surface area per cell volume. With metabolism being an important factor of the cellular phenotype, this work complements our recent mapping of the transcriptomic and proteomic changes towards a holistic description of the cellular phenotype during aging.