FEMS yeast research最新文献

Saccharomyces cerevisiae is a widely used yeast for industrial production of ethanol. However, elevated ethanol, temperature, and osmotic stress adversely affect fermentation efficiency. In this study, adaptive laboratory evolution for S. cerevisiae CEN.PK 113-7D on higher concentrations of ethanol was performed. After 144 days, the maximum specific growth rate (µmax) increased from 0.0240 to 0.1150 h-1 for the strain evolved on 9% v/v ethanol, and from 0.0002 to 0.0530 h-1 for the strain evolved on 11% v/v ethanol, and the specific glucose uptake rate increased by 30%. The strain evolved on 11% ethanol produced 94.5 g/L ethanol in a fermentation as compared to 78.5 g/L production by a non-evolved strain. By whole-genome sequencing of the evolved clones, we identified multiple coding mutations in genes involved in processes such as stress response, cell growth regulation, pentose phosphate pathway, lipid synthesis, and redox balance. The selected mutations in RKI1, CYC2, ANR2, RGA2, RGA1, LPX1, and LRE1 genes were validated by introducing them in the nonevolved yeast, showing 1.7-5-fold growth improvement at 9% ethanol (P < 0.05). Notably, RGA2, RGA1 and LPX 1 carried an identical missense mutation across three independent clones. The RKI1I208V mutant showed the highest ethanol tolerance, while CYC2N342A achieved the highest ethanol production.

As a traditional ethanol-producing microorganism, Saccharomyces cerevisiae is an ideal host for consolidated bioprocessing. However, when overloaded cellulase genes are expressed in yeast, the metabolic burden on cells may greatly affect cell growth and cellulosic ethanol production. In this study, we developed a yeast consortium system that secretes and assembles five types of cellulases on the yeast cell surface to improve cellulosic ethanol production. This system involves one display strain, which provides the scaffoldin on the surface and several secretion strains that secrete each cellulase. The secreted dockerin-containing enzymes, cellobiohydrolase (CBH), endoglucanase (EG), β-glucosidase (BGL), cellobiose dehydrogenase (CDH), and lytic polysaccharide monooxygenase (LPMO), were randomly assembled to the scaffoldin to generate a pentafunctional mini-cellulosome via cohesion-dockerin interactions. The developed system relieved the metabolic burden placed on the engineered single yeast strain and leveraged the innate metabolic potential of each host. In addition, the enzymes in the consortium acted synergistically and efficiently boosted cellulose degradation and ethanol production. When compared with the conventional system, this consortium system increased the ethanol titers from 2.66 to 4.11 g/l with phosphoric acid swollen cellulose (PASC) as the substrate, an improvement of 55%. With Avicel as the substrate, ethanol titers increased from 1.57 to 3.24 g/l, representing an enhancement of 106%.

In methylotrophic yeasts such as Komagataella phaffii (syn Pichia pastoris), the initial step of methanol metabolism by alcohol oxidase (Aox) generates hydrogen peroxide (H2O2) as a potentially toxic byproduct. Introduction of the ratiometric, genetically encoded fluorescent H2O2 biosensor HyPer7 in combination with cultivation in a microbioreactor allowed for the first time to in vivo determine H2O2 dynamics upon methanol utilization (MUT). In line monitoring of H2O2 during growth on glucose or methanol revealed a general increase in biosensor oxidation on methanol, with significant oxidation peaks shortly after methanol addition. HyPer7 also detected low endogenous H2O2 levels occurring during respiratory growth in K. phaffii and its signal responded to both external oxidants and reductants. In strains with different MUT phenotypes (K. phaffii deleted for aox1 and/or aox2), HyPer7 demonstrated that H2O2 production is mainly due to Aox1 activity, and explained why strains possessing only Aox2 (MutS) have superior growth and production capacities compared to the wild-type. In conclusion, we present the first application of an H2O2 biosensor in K. phaffii, offering new insights into methanol metabolism and oxidative stress. The findings hold promise for optimizing yeast cell factories and developing more sustainable production processes with reduced oxidative stress in the future.

The lag phase is a temporary, nonreplicative period observed when a microbial population is introduced to a new, nutrient-rich environment. Although the theoretical concept of growth phases is clear, the practical application of methods for estimating lag lengths is often challenging. In fact, there are two distinct assumptions: (i) that cells do not divide at all during the lag phase or (ii) that they divide but at a suboptimal rate. Therefore, the choice of method should consider not only technical limitations but also consistency with the biological context. Here, we investigate the performance of the most common lag estimation methods, using empirical and simulated datasets. We apply different biological scenarios and simulate curves with varying parameters (i.e. growth rate, noise level, and frequency of measurements) to test their impact on the estimated lag phase duration. Our validation shows that infrequent measurements, low growth rate, longer lag phases, or higher level of noise in the measurements result in higher bias and higher variance of lag estimation. Additionally, in case of noisy data, the methods relying on model fitting perform best.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas genome-wide screens are powerful tools for unraveling genotype-phenotype relationships, enabling precise manipulation of genes to study and engineer industrially useful traits. Traditional genetic methods, such as random mutagenesis or RNA interference, often lack the specificity and scalability required for large-scale functional genomic screens. CRISPR systems overcome these limitations by offering precision gene targeting and manipulation, allowing for high-throughput investigations into gene function and interactions. Recent work has shown that CRISPR genome editing is widely adaptable to several yeast species, many of which have natural traits suited for industrial biotechnology. In this review, we discuss recent advances in yeast functional genomics, emphasizing advancements made with CRISPR tools. We discuss how the development and optimization of CRISPR genome-wide screens have enabled a host-first approach to metabolic engineering, which takes advantage of the natural traits of nonconventional yeast-fast growth rates, high stress tolerance, and novel metabolism-to create new production hosts. Lastly, we discuss future directions, including automation and biosensor-driven screens, to enhance high-throughput CRISPR-enabled yeast engineering.

Most laboratory strains of the yeast Saccharomyces cerevisiae are incapable of invading agar, to form large colonies (mats), and to develop filament-like structures (pseudohyphae). A prominent strain that manifests these morphologies is ∑1278b. While induced transcription of the FLO11 gene is critical for executing invasive growth, mat formation, and pseudohyphal growth, downregulation of the 'general stress response' also seems to be required. As this response is weak in ∑1278b cells, we assumed that they may be sensitives to stresses. We report, however, that they are resistant to various stressors, but severely sensitive specifically to NaCl. We found that this sensitivity is a result of mutations in the single ∑1278b's ENA gene, encoding P-type sodium ATPase. Other laboratory strains harbor three to five copies of ENA, suggesting that ∑1278b was selected against Ena activity. Obtaining ∑1278b cells that can grow on NaCl allows checking its effect on colony morphologies. In the presence of NaCl, ∑1278b/ENA1+ cells do not invade agar, and do not form pseudohyphae or mats. Thus, we have found the following: (i) The ∑1278b strain differs from other laboratory strains with respect to sensitivity to NaCl, because it has no active Na+ ATPase exporter. (ii) NaCl is a suppressor of invasiveness, filamentous growth, and mat formation.

The production of second-generation (2 G) bioethanol, a key sector in industrial biotechnology, addresses the demand for sustainable energy by utilizing lignocellulosic biomass. Efficient fermentation of all sugars from lignocellulose hydrolysis is essential to enhance ethanol titers, improve biomass-to-biofuel yields, and lower costs. This review compares the potential of recombinant yeast strains for 2 G bioethanol production, focusing on their ability to metabolize diverse sugars, particularly xylose. Saccharomyces cerevisiae, engineered for enhanced pentose and hexose utilization, is compared with the nonconventional yeasts Scheffersomyces stipitis, Kluyveromyces marxianus, and Ogataea polymorpha. Key factors include sugar assimilation pathways, cofermentation with glucose, oxygen requirements, tolerance to hydrolysate inhibitors, and process temperature. Saccharomyces cerevisiae shows high ethanol tolerance but requires genetic modification for xylose use. Scheffersomyces stipitis ferments xylose naturally but lacks robustness. Kluyveromyces marxianus offers thermotolerance and a broad substrate range with lower ethanol yields, while O. polymorpha enables high-temperature fermentation but yields modest ethanol from xylose. The comparative analysis clarifies each yeast's advantages and limitations, supporting the development of more efficient 2 G bioethanol production strategies. Strain selection must balance ethanol yield, stress tolerance, and temperature adaptability to meet industrial requirements for cost-effective lignocellulosic bioethanol production.

Although endo-lysosomal abnormalities have been recognized as a pathognomonic feature of Alzheimer's disease, the lack of druggable targets has hampered the translation from bench to bedside. This article provides an overview of the insights gained from yeast research with a focus on understudied luminal acidification mechanisms and their major impact on disease progression. The yeast-to-human discovery and validation strategy identified a "druggable" triad featuring luminal pH, sterol content, and trafficking that (dys)regulate reciprocally. Endosomal Na+/H+ exchangers (eNHE), discovered in yeast and later described in mammals, provide independent support for this pathogenic model. The brain is often the most severely affected organ in patients with eNHE mutations, and a subset is causally linked to progressive and severe neurodegeneration, demonstrating that neurons heavily rely on fine-tuning of endosomal pH. We present recent advances on the role of eNHE in ageing related neurodegenerative diseases, which has implications for pathogenesis and therapy. Future studies should unravel the broader landscape of endo-lysosomal pH in neurodegenerative diseases. Given that pharmacologic correction of luminal hyperacidification defect completely ameliorates endo-lysosomal deficits in eNHE deletion yeast, there is compelling reason to believe that efforts to target endo-lysosomal acid-base homeostasis will eventually lead to novel therapeutic approaches for neurodegenerative diseases.