Saccharomyces cerevisiae
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ABSTRACT:
PROVIDER: PRJNA152923 | ENA |
REPOSITORIES: ENA
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Project description:The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources Two conditions (glucose and galactose) with two biological replicates for S. cerevisiae strains S288c and CEN.PK113-7D
2010-12-22 | E-GEOD-21479 | biostudies-arrayexpress
Project description:Thermotolerance development of robust Saccharomyces cerevisiae is necessary to enhance enzyme activity of cellulase, lower cooling costs, and reduce cell harm from the bad-distributed heat transfer in large-scale fermentation. The process-based studies of adaptive evolution have been well documented, but it remains unknown for the underlying molecular mechanism of the improved thermotolerance and the facilitated ethanol fermentability derived from adaptive evolution. Here, a robust thermotolerant S. cerevisiae Z100 was obtained with significantly improved ethanol fermentability under the stress of high temperature (50 oC) after 91 days’ adaptive evolution. RNA sequencing showed that adaptive evolution and its derived thermotolerance contributed to the unique gene transcriptional landscapes of the evolved strain. An interesting phenomenon was that the gene transcriptional signals of carbon metabolism were strengthened not at 50 oC but at 30 oC in S. cerevisiae Z100, and thus suggested that the improved thermotolerance led to the enhanced ethanol fermentability at 30 oC. The deeply repressed gene transcriptional expression indicated ribosome would be another key thermotolerant mechanism for the evolved strain. This study would provide a robust thermotolerant S. cerevisiae for bioethanol production and an important clue for future synthetic biology to thermotolerance engineering of fermentation strains.
2021-08-20 | GSE182342 | GEO
Project description:Carotenoids are a large family of health-beneficial compounds that have been widely used in the food and nutraceutical industries. There have been extensive studies to engineer Saccharomyces cerevisiae for the production of carotenoids, which already gained high level. However, it was difficult to discover new targets that were relevant to the accumulation of carotenoids. Herein, a new, ethanol-induced adaptive laboratory evolution was applied to boost carotenoid accumulation in a carotenoid producer BL03-D-4, subsequently, an evolved strain M3 was obtained with a 5.1-fold increase in carotenoid yield. Through whole-genome resequencing and reverse engineering, loss-of-function mutation of phosphofructokinase 1 (PFK1) was revealed as the major cause of increased carotenoid yield. Transcriptome analysis was conducted to reveal the potential mechanisms for improved yield, and strengthening of gluconeogenesis and downregulation of cell wall-related genes were observed in M3. This study provided a classic case where the appropriate selective pressure could be employed to improve carotenoid yield using adaptive evolution and elucidated the causal mutation of evolved strain.
2021-01-09 | GSE164470 | GEO
Project description:The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources
2010-12-22 | GSE21479 | GEO
Project description:Reconstructed mutants of yeast by inverse metabolic engineering were characterized by fermentation physiology and tools from systems biology.
2012-02-29 | GSE36118 | GEO
Project description:Reconstructed mutants of yeast by inverse metabolic engineering were characterized by fermentation physiology and tools from systems biology. Six reconstructed mutants of yeast were grown on aerobic batch with galactose as carbon source
2012-02-29 | E-GEOD-36118 | biostudies-arrayexpress
Project description:Adaptive laboratory evolution and reverse engineering of low pH tolerance in Saccharomyces cerevisiae
| PRJNA546594 | ENA
Project description:Although the relationship between phenotypic plasticity and evolutionary dynamics has attracted large interest, very little is known about the contribution of phenotypic plasticity to adaptive evolution. In this study, we analyzed phenotypic and genotypic changes in E. coli cells during adaptive evolution to ethanol stress. To quantify the phenotypic changes, transcriptome analyses were performed. We previously obtained 6 independently evolved ethanol tolerant E. coli strains, strains A through F, by culturing cells under 5% ethanol stress for about 1000 generations and found a significantly larger growth rate than the parent strains (Horinouchi et al, 2010, PMID: 20955615). To elucidate the phenotypic changes that occurred during adaptive evolution, we quantified the time-series of the expression changes by microarray analysis. Starting from frozen stocks obtained at 6 time points (0, 384, 744, 1224, 1824 and 2496 hours) in laboratory evolution, cells were cultured under 5% ethanol stress, and mRNA samples were obtained in the exponential growth phase for microarray analysis.
2014-07-21 | E-GEOD-59050 | biostudies-arrayexpress
Project description:Adaptive laboratory evolution is highly effective for improving desired traits through natural selection. However, its applicability is inherently constrained to growth-correlated traits precluding traits of interest that incur a fitness cost, such as metabolite secretion. Here, we introduce the concept of tacking trait enabling natural selection of fitness-costly metabolic traits. The concept is inspired from the tacking maneuver used in sailing for traversing upwind. We use first-principle metabolic models to design an evolution niche wherein the tacking trait and fitness become correlated. Adaptive evolution in this niche, when followed by the reversal to the original niche, manifests in the improvement of the desired trait due to biochemical coupling between the tacking and the desired trait. We experimentally demonstrated this strategy, termed EvolveX, by evolving wine yeasts for increased aroma production. RNA-sequencing was performed for parental and evolved strains in the respective evolution niche and in natural grape must.
2021-12-30 | E-MTAB-10019 | biostudies-arrayexpress