Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 degrees C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naive Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 mu g/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.Ines Mendes and Ricardo Franco-Duarte are recipients of a fellowship from the Portuguese Science Foundation, FCT (SFRH/BD/74798/2010, SFRH/BD/48591/2008, respectively) and Joao Drumonde-Neves is recipient of a fellowship from the Azores government (M3.1.2/F/006/2008 (DRCT)). Financial support was obtained from FEDER funds through the program COMPETE and by national funds through FCT by the projects FCOMP-01-0124-008775 (PTDC/AGR-ALI/103392/2008) and PTDC/AGR-ALI/121062/2010. Lan Umek and Blaz Zupan acknowledge financial support from Slovene Research Agency (P2-0209). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GP

Differences in environmental stress response between yeasts is consistent with species-specific lifestyles

Defining how organisms respond to environmental change has always been an important step toward understating their adaptive capacity and physiology. Variation in transcription during stress has been widely described in model species, especially in the yeastSaccharomyces cerevisiae, which helped to shape general rules regarding how cells cope with environmental constraints as well as decipher the functions of many genes. Now, comparison of the environmental stress response (ESR) across species is essential to obtain a better insight into the common and species-specific features of stress defense. In this context, we explored the transcriptional landscape of the yeastLachancea kluyveri(formerlySaccharomyces kluyveri) in response to diverse stresses, using RNA-seq. We investigated variation in gene expression and observed a link between genetic plasticity and environmental sensitivity. We identified the ESR genes in this species and compared them to those already found inS. cerevisiae We observed common features between the two species as well as divergence in the regulatory networks involved. Interestingly, some changes were related to differences in species lifestyle. Thus, we were able to decipher how adaptation to stress has evolved among different yeast species. Finally, by analyzing patterns of coexpression, we were able to propose potential biological functions for 42% of genes and furthermore annotate 301 genes for which no function could be assigned by homology. This large dataset allowed for the characterization of the evolution of gene regulation and provides an efficient tool to assess gene function

On the complexity of the Saccharomyces bayanus taxon: hybridization and potential hybrid speciation

Although the genus Saccharomyces has been thoroughly studied, some species in the genus has not yet been accurately resolved; an example is S. bayanus, a taxon that includes genetically diverse lineages of pure and hybrid strains. This diversity makes the assignation and classification of strains belonging to this species unclear and controversial. They have been subdivided by some authors into two varieties (bayanus and uvarum), which have been raised to the species level by others. In this work, we evaluate the complexity of 46 different strains included in the S. bayanus taxon by means of PCR-RFLP analysis and by sequencing of 34 gene regions and one mitochondrial gene. Using the sequence data, and based on the S. bayanus var. bayanus reference strain NBRC 1948, a hypothetical pure S. bayanus was reconstructed for these genes that showed alleles with similarity values lower than 97% with the S. bayanus var. uvarum strain CBS 7001, and of 99¿100% with the non S. cerevisiae portion in S. pastorianus Weihenstephan 34/70 and with the new species S. eubayanus. Among the S. bayanus strains under study, different levels of homozygosity, hybridization and introgression were found; however, no pure S. bayanus var. bayanus strain was identified. These S. bayanus hybrids can be classified into two types: homozygous (type I) and heterozygous hybrids (type II), indicating that they have been originated by different hybridization processes. Therefore, a putative evolutionary scenario involving two different hybridization events between a S. bayanus var. uvarum and unknown European S. eubayanus-like strains can be postulated to explain the genomic diversity observed in our S. bayanus var. bayanus strains

Whole-Genome Sequencing of Sake Yeast Saccharomyces cerevisiae Kyokai no. 7

The term ‘sake yeast’ is generally used to indicate the Saccharomyces cerevisiae strains that possess characteristics distinct from others including the laboratory strain S288C and are well suited for sake brewery. Here, we report the draft whole-genome shotgun sequence of a commonly used diploid sake yeast strain, Kyokai no. 7 (K7). The assembled sequence of K7 was nearly identical to that of the S288C, except for several subtelomeric polymorphisms and two large inversions in K7. A survey of heterozygous bases between the homologous chromosomes revealed the presence of mosaic-like uneven distribution of heterozygosity in K7. The distribution patterns appeared to have resulted from repeated losses of heterozygosity in the ancestral lineage of K7. Analysis of genes revealed the presence of both K7-acquired and K7-lost genes, in addition to numerous others with segmentations and terminal discrepancies in comparison with those of S288C. The distribution of Ty element also largely differed in the two strains. Interestingly, two regions in chromosomes I and VII of S288C have apparently been replaced by Ty elements in K7. Sequence comparisons suggest that these gene conversions were caused by cDNA-mediated recombination of Ty elements. The present study advances our understanding of the functional and evolutionary genomics of the sake yeast

Phenotypic Landscape of Saccharomyces cerevisiae during Wine Fermentation: Evidence for Origin-Dependent Metabolic Traits

The species Saccharomyces cerevisiae includes natural strains, clinical isolates, and a large number of strains used in human activities. The aim of this work was to investigate how the adaptation to a broad range of ecological niches may have selectively shaped the yeast metabolic network to generate specific phenotypes. Using 72 S. cerevisiae strains collected from various sources, we provide, for the first time, a population-scale picture of the fermentative metabolic traits found in the S. cerevisiae species under wine making conditions. Considerable phenotypic variation was found suggesting that this yeast employs diverse metabolic strategies to face environmental constraints. Several groups of strains can be distinguished from the entire population on the basis of specific traits. Strains accustomed to growing in the presence of high sugar concentrations, such as wine yeasts and strains obtained from fruits, were able to achieve fermentation, whereas natural yeasts isolated from “poor-sugar” environments, such as oak trees or plants, were not. Commercial wine yeasts clearly appeared as a subset of vineyard isolates, and were mainly differentiated by their fermentative performances as well as their low acetate production. Overall, the emergence of the origin-dependent properties of the strains provides evidence for a phenotypic evolution driven by environmental constraints and/or human selection within S. cerevisiae

Correction: AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae

The characterization and public release of genome sequences from thousands of organisms is expanding the scope for genetic variation studies. However, understanding the phenotypic consequences of genetic variation remains a challenge in eukaryotes due to the complexity of the genotype-phenotype map. One approach to this is the intensive study of model systems for which diverse sources of information can be accumulated and integrated. Saccharomyces cerevisiae is an extensively studied model organism, with well-known protein functions and thoroughly curated phenotype data. To develop and expand the available resources linking genomic variation with function in yeast, we aim to model the pan-genome of S. cerevisiae. To initiate the yeast pan-genome, we newly sequenced or re-sequenced the genomes of 25 strains that are commonly used in the yeast research community using advanced sequencing technology at high quality. We also developed a pipeline for automated pan-genome analysis, which integrates the steps of assembly, annotation, and variation calling. To assign strain-specific functional annotations, we identified genes that were not present in the reference genome. We classified these according to their presence or absence across strains and characterized each group of genes with known functional and phenotypic features. The functional roles of novel genes not found in the reference genome and associated with strains or groups of strains appear to be consistent with anticipated adaptations in specific lineages. As more S. cerevisiae strain genomes are released, our analysis can be used to collate genome data and relate it to lineage-specific patterns of genome evolution. Our new tool set will enhance our understanding of genomic and functional evolution in S. cerevisiae, and will be available to the yeast genetics and molecular biology community