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Yeast cell wall chitin has been shown to bind grape pathogenesis-related chitinases that are the primary cause of protein haze in wines, suggesting that yeast cell walls may be applied for haze protection. Here, we present a high-throughput screen to identify yeast strains with high cell wall chitin using a reiterative enrichment strategy and fluorescence-activated cell sorting of cells labelled with either GFP-tagged chitinase or Calcofluor white. To assess the validity of the strategy, we first used a pooled deletion strain library of Saccharomyces cerevisiae. The strategy enriched for deletion mutants with genes that had previously been described as having an impact on chitin levels. Genes that had not previously been linked to chitin biosynthesis or deposition were also identified. These genes are involved in cell wall maintenance and/or membrane trafficking functions. The strategy was then applied to a mutagenized population of a commercial wine yeast strain, S. cerevisiae EC1118. Enriched mutant strains showed significantly higher cell wall chitin than the wild type and significantly reduced the activity of chitinases in synthetic model wine, suggesting that these strains may be able to reduce haze formation in wine., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

Yeast and microalgae are microorganisms with widely diverging physiological and biotechnological properties. Accordingly, their fields of applications diverge: yeasts are primarily applied in processes related to fermentation, while microalgae are used for the production of high-value metabolites and green technologies such as carbon capture. Heterotrophic-autotrophic systems and synthetic ecology approaches have been proposed as tools to achieve stable combinations of such evolutionarily unrelated species. We describe an entirely novel synthetic ecology-based approach to evolve co-operative behaviour between winery wastewater isolates of the yeast Saccharomyces cerevisiae and microalga Chlorella sorokiniana. The data show that biomass production and mutualistic growth improved when co-evolved yeast and microalgae strains were paired together. Combinations of co-evolved strains displayed a range of phenotypes, including differences in amino acid profiles. Taken together, the results demonstrate that biotic selection pressures can lead to improved mutualistic growth phenotypes over relatively short time periods.

In the macroscopic world, ecological interactions between multiple species of fauna and flora are recognised as major role-players in the evolution of any particular species. By comparison, research on ecological interactions as a driver of evolutionary adaptation in microbial ecosystems has been neglected. The evolutionary history of the budding yeast Saccharomyces cerevisiae has been extensively researched, providing an unmatched foundation for exploring adaptive evolution of microorganisms. However, in most studies, the habitat is only defined by physical and chemical parameters, and little attention is paid to the impact of cohabiting species. Such ecological interactions arguably provide a more relevant evolutionary framework. Within the genomic phylogenetic tree of S. cerevisiae strains, wine associated isolates form a distinct clade, also matched by phenotypic evidence. This domestication signature in genomes and phenomes suggests that the wine fermentation environment is of significant evolutionary relevance. Data also show that the microbiological composition of wine fermentation ecosystems is dominated by the same species globally, suggesting that these species have co-evolved within this ecosystem. This system therefore presents an excellent model for investigating the origins and mechanisms of interspecific yeast interactions. This review explores the role of biotic stress in the adaptive evolution of wine yeast., (© FEMS 2019.)

A simple model is proposed to account for the loss of collected X-ray signal by the shadowing of X-ray detectors in the scanning transmission electron microscope. The model is intended to aid the analysis of three-dimensional elemental data sets acquired using energy-dispersive X-ray tomography methods where shadow-free specimen holders are unsuitable or unavailable. The model also provides a useful measure of the detection system geometry.

Energy resolution is one of the most important parameters in electron energy-loss spectroscopy. This is especially true for measurement of surface plasmon resonances, where high-energy resolution is crucial for resolving individual resonance peaks, in particular close to the zero-loss peak. In this work, we improve the energy resolution of electron energy-loss spectra of surface plasmon resonances, acquired with a monochromated beam in a scanning transmission electron microscope, by the use of the Richardson-Lucy deconvolution algorithm. We test the performance of the algorithm in a simulated spectrum and then apply it to experimental energy-loss spectra of a lithographically patterned silver nanorod. By reduction of the point spread function of the spectrum, we are able to identify low-energy surface plasmon peaks in spectra, more localized features, and higher contrast in surface plasmon energy-filtered maps. Thanks to the combination of a monochromated beam and the Richardson-Lucy algorithm, we improve the effective resolution down to 30 meV, and evidence of success up to 10 meV resolution for losses below 1 eV. We also propose, implement, and test two methods to limit the number of iterations in the algorithm. The first method is based on noise measurement and analysis, while in the second we monitor the change of slope in the deconvolved spectrum.

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes.

Fifteen liposarcomas from 13 patients were examined by electron microscopy. These included nine primary tumors, four recurrent tumors after primary surgery or irradiation, and two metastatic lesions. Twelve of the liposarcomas were located in the thigh, and 11 were of the myxoid variety. All neoplasms were composed of cells having the ultrastructural characteristics of some stage of lipoblastic differentiation, i.e., lipid droplets, micropinocytotic vesicles, glycogen, external lamina, intermediate filaments, Golgi apparatuses, rough and smooth endoplasmic reticulum, and mitochondria. The nuclear-cytoplasmic ratio and nuclear pleomorphism were related inversely to the size and number of lipid droplets. Lipoblasts were frequently in close association with capillaries and pericytes, and in four cases lipid droplets were found in pericytes. Multivacuolated, mitochondria-rich lipoblasts, resembling brown fat cells, also were seen. Most tumors contained lipid-free, poorly differentiated mesenchymal cells that showed a continuum of morphologic differentiation to cells that closely resembled early lipoblasts that contained nonmembrane-bound lipid vacuoles. Fibrolipoblasts, cells containing lipid droplets and abundant rough endoplasmic reticulum, were observed only in well-differentiated liposarcomas. Some soft tissue sarcomas contain vacuolated cells that simulate lipoblasts by light microscopy but prove to be reactive or malignant fibroblasts, histiocytes, or smooth muscle cells ultrastructurally. Therefore, use of electron microscopy may be necessary to establish the line of differentiation in these neoplasms.