Phenotypic Diagnosis of Lineage and Differentiation During Sake Yeast Breeding

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

Improved 2,3-Butanediol Production Rate of Metabolically Engineered Saccharomyces cerevisiae by Deletion of RIM15 and Activation of Pyruvate Consumption Pathway

Saccharomyces cerevisiae is a promising host for the bioproduction of higher alcohols, such as 2,3-butanediol (2,3-BDO). Metabolically engineered S. cerevisiae strains that produce 2,3-BDO via glycolysis have been constructed. However, the specific 2,3-BDO production rates of engineered strains must be improved. To identify approaches to improving the 2,3-BDO production rate, we investigated the factors contributing to higher ethanol production rates in certain industrial strains of S. cerevisiae compared to laboratory strains. Sequence analysis of 11 industrial strains revealed the accumulation of many nonsynonymous substitutions in RIM15, a negative regulator of high fermentation capability. Comparative metabolome analysis suggested a positive correlation between the rate of ethanol production and the activity of the pyruvate-consuming pathway. Based on these findings, RIM15 was deleted, and the pyruvate-consuming pathway was activated in YHI030, a metabolically engineered S. cerevisiae strain that produces 2,3-BDO. The titer, specific production rate, and yield of 2,3-BDO in the test tube-scale culture using the YMS106 strain reached 66.4 ± 4.4 mM, 1.17 ± 0.017 mmol (g dry cell weight h)-1, and 0.70 ± 0.03 mol (mol glucose consumed)-1. These values were 2.14-, 2.92-, and 1.81-fold higher than those of the vector control, respectively. These results suggest that bioalcohol production via glycolysis can be enhanced in a metabolically engineered S. cerevisiae strain by deleting RIM15 and activating the pyruvate-consuming pathway.

Development of sake yeast breeding and analysis of genes related to its various phenotypes

Sake is a traditional Japanese alcoholic beverage made from rice and water, fermented by the filamentous fungi Aspergillus oryzae and the yeast Saccharomyces cerevisiae. Yeast strains, also called sake yeasts, with high alcohol yield and the ability to produce desired flavor compounds in the sake, have been isolated from the environment for more than a century. Furthermore, numerous methods to breed sake yeasts without genetic modification have been developed. The objectives of breeding include increasing the efficiency of production, improving the aroma and taste, enhancing safety, imparting functional properties, and altering the appearance of sake. With the recent development of molecular biology, the suitable sake brewing characteristics in sake yeasts, and the causes of acquisition of additional phenotypes in bred yeasts have been elucidated genetically. This mini-review summarizes the history and lineage of sake yeasts, their genetic characteristics, the major breeding methods used, and molecular biological analysis of the acquired strains. The data in this review on the metabolic mechanisms of sake yeasts and their genetic profiles will enable the development of future strains with superior phenotypes.

Measurement and Characterization of Yeast Cell Size Using a Digital Optical Microscope

Yeast produces flavor components as by-products of fermentation. Such components, which account for considerable yeast quality value, may influence cell membrane fluidity and regulate cell size. In this study, yeast cell size was measured using various methods, and cell sizes were characterized for each strain of yeast. Herein, a method for measuring the cell size and automatically acquiring the cell area was proposed using inexpensive methylene blue staining and a versatile digital microscope to automatically acquire the cell area. To evaluate the efficiency, data on cell length and area were compared using the conventional and proposed methods. Additionally, the advantages of automated measurement and the ability to count cell numbers in the future are discussed through the acquisition of cell size data, which would be required for microfabrication and cell fabrication techniques. Although there are various candidate methods for evaluating the properties of each yeast strain, the present technique may be useful for evaluating the productivity of flavor components and selecting a yeast strain for producing food and drink products with high commodity values.

Genome Editing to Generate Sake Yeast Strains with Eight Mutations That Confer Excellent Brewing Characteristics

Sake yeast is mostly diploid, so the introduction of recessive mutations to improve brewing characteristics requires considerable effort. To construct sake yeast with multiple excellent brewing characteristics, we used an evidence-based approach that exploits genome editing technology. Our breeding targeted the AWA1, CAR1, MDE1, and FAS2 genes. We introduced eight mutations into standard sake yeast to construct a non-foam-forming strain that makes sake without producing carcinogens or an unpleasant odor, while producing a sweet ginjo aroma. Small-scale fermentation tests showed that the desired sake could be brewed with our genome-edited strains. The existence of a few unexpected genetic perturbations introduced during breeding proved that genome editing technology is extremely effective for the serial breeding of sake yeast.

Cloning of the SPO11 gene that complements a meiotic recombination defect in sake yeast

Cross hybridization breeding of sake yeasts is hampered by difficulty in acquisition of haploid cells through sporulation. We previously demonstrated that typical sake yeast strains were defective in meiotic chromosome recombination, which caused poor sporulation and loss of spore viability. In this study, we screened a single copy plasmid genomic DNA library of the laboratory Saccharomyces cerevisiae GRF88 for genes that might complement the meiotic recombination defect of UTCAH-3, a strain derived from the sake yeast Kyokai no. 7 (K7). We identified the SPO11 gene of the laboratory strain (ScSPO11), encoding a meiosis-specific endonuclease that catalyzes DNA double-strand breaks required for meiotic recombination, as a gene that restored meiotic recombination and spore viability of UTCAH-3. K7SPO11 could not restore sporulation efficiency and spore viability of UTCAH-3 and a laboratory strain BY4743 spo11Δ/spo11Δ, indicating that K7SPO11 is not functional. Sequence analysis of the SPO11 genes of various Kyokai sake yeasts (K1, and K3-K10) revealed that the K7 group of sake yeasts (K6, K7, K9, and K10) had a mutual missense mutation (C73T) in addition to other three common mutations present in all Kyokai yeasts tested. ScSPO11^C73T created through in vitro mutagenesis could not restore spore viability of BY4743 spo11Δ/spo11Δ. On the other hand, K8SPO11, which have the three common mutations except for C73T could restore spore viability of BY4743 spo11Δ/spo11Δ. These results suggest that C73T might be a causative mutation of recombination defect in K7SPO11. Moreover, we found that the introduction of ScRIM15 restored sporulation efficiency but not spore viability.

Intelligent image-activated cell sorting 2.0

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

Quantitative Trait Nucleotides Impacting the Technological Performances of Industrial Saccharomyces cerevisiae Strains

The budding yeast Saccharomyces cerevisiae is certainly the prime industrial microorganism and is related to many biotechnological applications including food fermentations, biofuel production, green chemistry, and drug production. A noteworthy characteristic of this species is the existence of subgroups well adapted to specific processes with some individuals showing optimal technological traits. In the last 20 years, many studies have established a link between quantitative traits and single-nucleotide polymorphisms found in hundreds of genes. These natural variations constitute a pool of QTNs (quantitative trait nucleotides) that modulate yeast traits of economic interest for industry. By selecting a subset of genes functionally validated, a total of 284 QTNs were inventoried. Their distribution across pan and core genome and their frequency within the 1,011 Saccharomyces cerevisiae genomes were analyzed. We found that 150 of the 284 QTNs have a frequency lower than 5%, meaning that these variants would be undetectable by genome-wide association studies (GWAS). This analysis also suggests that most of the functional variants are private to a subpopulation, possibly due to their adaptive role to specific industrial environment. In this review, we provide a literature survey of their phenotypic impact and discuss the opportunities and the limits of their use for industrial strain selection.

Genome editing to generate nonfoam-forming sake yeast strains

Mutations frequently occur during breeding of sake yeasts and result in unexpected phenotypes. Here, genome editing tools were applied to develop an ideal nonfoam-forming sake yeast strain, K7GE01, which had homozygous awa1∆/awa1∆ deletion alleles that were responsible for nonfoam formation and few off-target mutations. High-dimensional morphological phenotyping revealed no detectable morphological differences between the genome-edited strain and its parent, while the canonical nonfoam-forming strain, K701, showed obvious morphological changes. Small-scale fermentation tests also showed differences between components of sake produced by K7GE01 and K701. The K7GE01 strain produced sake with significant differences in the concentrations of ethyl acetate, malic acid, lactic acid, and acetic acid, while K701 produced sake with more differences. Our results indicated genuine phenotypes of awa1∆/awa1∆ in sake yeast isolates and showed the usefulness of genome editing tools for sake yeast breeding.

Progress in the genomics and genome-wide study of sake yeast

Completion of the whole genome sequence of a laboratory yeast strain Saccharomyces cerevisiae in 1996 ushered in the development of genome-wide experimental tools and accelerated subsequent genetic study of S. cerevisiae. The study of sake yeast also shared the benefit of such tools as DNA microarrays, gene disruption-mutant collections, and others. Moreover, whole genome analysis of representative sake yeast strain Kyokai no. 7 was performed in the late 2000s, and enabled comparative genomics between sake yeast and laboratory yeast, resulting in some notable finding for of sake yeast genetics. Development of next-generation DNA sequencing and bioinformatics also drastically changed the field of the genetics, including for sake yeast. Genomics and the genome-wide study of sake yeast have progressed under these circumstances during the last two decades, and are summarized in this article. Abbreviations: AFLP: amplified fragment length polymorphism; CGH: comparative genomic hybridization; CNV: copy number variation; DMS: dimethyl succinate; DSW: deep sea water; LOH: loss of heterozygosity; NGS: next generation sequencer; QTL: quantitative trait loci; QTN: quantitative trait nucleotide; SAM: S-adenosyl methionine; SNV: single nucleotide variation.

André Goffeau's imprinting on second generation yeast "genomologists"

All authors of the present paper have worked in labs that participated to the sequencing effort of the Saccharomyces cerevisiae reference genome, and we owe to this the fact that we have all chosen to work on genomics of yeasts. S. cerevisiae has been a popular model species for genetics since the 20th century as well as being a model for general eukaryotic cellular processes. Although it has also been used empirically in fermentation for millennia, there was until recently, a lack of knowledge about the natural and evolutionary history of this yeast. The achievement of the international effort to sequence its genome was the foundation for understanding many eukaryotic biological processes but also represented the first step towards the study of the genome and ecological diversity of yeast populations worldwide. We will describe recent advances in yeast comparative and population genomics that find their origins in the S. cerevisiae genome project initiated and pursued by André Goffeau.

Nutrient Signaling via the TORC1-Greatwall-PP2AB55δ Pathway Is Responsible for the High Initial Rates of Alcoholic Fermentation in Sake Yeast Strains of Saccharomyces cerevisiae

Saccharomyces cerevisiae sake yeast strain Kyokai no. 7 (K7) and its relatives carry a homozygous loss-of-function mutation in the RIM15 gene, which encodes a Greatwall family protein kinase. Disruption of RIM15 in nonsake yeast strains leads to improved alcoholic fermentation, indicating that the defect in Rim15p is associated with the enhanced fermentation performance of sake yeast cells. In order to understand how Rim15p mediates fermentation control, we here focused on target-of-rapamycin protein kinase complex 1 (TORC1) and protein phosphatase 2A with the B55δ regulatory subunit (PP2A^B55δ), complexes that are known to act upstream and downstream of Rim15p, respectively. Several lines of evidence, including our previous transcriptomic analysis data, suggested enhanced TORC1 signaling in sake yeast cells during sake fermentation. Fermentation tests of the TORC1-related mutants using a laboratory strain revealed that TORC1 signaling positively regulates the initial fermentation rate in a Rim15p-dependent manner. Deletion of the CDC55 gene, encoding B55δ, abolished the high fermentation performance of Rim15p-deficient laboratory yeast and sake yeast cells, indicating that PP2A^B55δ mediates the fermentation control by TORC1 and Rim15p. The TORC1-Greatwall-PP2A^B55δ pathway similarly affected the fermentation rate in the fission yeast Schizosaccharomyces pombe, strongly suggesting that the evolutionarily conserved pathway governs alcoholic fermentation in yeasts. It is likely that elevated PP2A^B55δ activity accounts for the high fermentation performance of sake yeast cells. Heterozygous loss-of-function mutations in CDC55 found in K7-related sake strains may indicate that the Rim15p-deficient phenotypes are disadvantageous to cell survival.IMPORTANCE The biochemical processes and enzymes responsible for glycolysis and alcoholic fermentation by the yeast S. cerevisiae have long been the subject of scientific research. Nevertheless, the factors determining fermentation performance in vivo are not fully understood. As a result, the industrial breeding of yeast strains has required empirical characterization of fermentation by screening numerous mutants through laborious fermentation tests. To establish a rational and efficient breeding strategy, key regulators of alcoholic fermentation need to be identified. In the present study, we focused on how sake yeast strains of S. cerevisiae have acquired high alcoholic fermentation performance. Our findings provide a rational molecular basis to design yeast strains with optimal fermentation performance for production of alcoholic beverages and bioethanol. In addition, as the evolutionarily conserved TORC1-Greatwall-PP2A^B55δ pathway plays a major role in the glycolytic control, our work may contribute to research on carbohydrate metabolism in higher eukaryotes.

Genome evolution across 1,011 Saccharomyces cerevisiae isolates

Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single 'out-of-China' origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype-phenotype studies in this classic model system.