High-dimensional and large-scale phenotyping of yeast mutants

Imaging-based profiling for elucidation of antibacterial mechanisms of action

In this review, we aim to summarize experimental data and approaches to identifying cellular targets or mechanisms of action of antibacterials based on imaging techniques. Imaging-based profiling methods, such as bacterial cytological profiling, dynamic bacterial morphology imaging, and others, have become a useful research tool for mechanistic studies of new antibiotics as well as combinations with conventional ones and other therapeutic options. The main methodological and experimental details and obtained results are summarized and discussed. The review covers the literature up to February 2024.

Imaging flow cytometry-based cellular screening elucidates pathophysiology in individuals with Variants of Uncertain Significance

BACKGROUND

Deciphering variants of uncertain significance (VUS) represents a major diagnostic challenge, partially due to the lack of easy-to-use and versatile cellular readouts that aid the interpretation of pathogenicity and pathophysiology. To address this challenge, we propose a high-throughput screening of cellular functionality through an imaging flow cytometry (IFC)-based platform.

METHODS

Six assays to evaluate autophagic-, lysosomal-, Golgi- health, mitochondrial function, ER stress, and NF-κβ activity were developed in fibroblasts. Assay sensitivity was verified with compounds (N = 5) and positive control patients (N = 6). Eight healthy controls and 20 individuals with VUS were screened.

RESULTS

All molecular compounds and positive controls showed significant changes on their cognate assays, confirming assay sensitivity. Simultaneous screening of positive control patients on all six assays revealed distinct phenotypic profiles. In addition, individuals with VUS(es) in well-known disease genes showed distinct - but similar-phenotypic profiles compared to patients with pathogenic variants in the same gene.. For all individuals with VUSes in Genes of Uncertain Significance (GUS), we found one or more of six assays were significantly altered. Broadening the screening to an untargeted approach led to the identification of two clusters that allowed for the recognition of altered cell cycle dynamics and DNA damage repair defects. Experimental follow-up of the 'DNA damage repair defect cluster' led to the discovery of highly specific defects in top2cc release from double-strand DNA breaks in one of these individuals, harboring a VUS in the RAD54L2 gene.

CONCLUSIONS

Our high-throughput IFC-based platform simplifies the process of identifying VUS pathogenicity through six assays and allows for the recognition of useful pathophysiological markers that structure follow-up experiments, thereby representing a novel valuable tool for precise functional diagnostics in genomics.

SSBD: an ecosystem for enhanced sharing and reuse of bioimaging data

SSBD (https://ssbd.riken.jp) is a platform for the sharing and reuse of bioimaging data. As part of efforts to build a bioimaging data ecosystem, SSBD has recently been updated to a two-tiered data resource comprising SSBD:repository, a public repository for the sharing of all types of bioimaging data reported in journals, and SSBD:database, an added-value database for the sharing of curated, highly reusable, metadata-rich data. This update addresses the conflicting demands of rapid data publication and sharing of richly annotated data, thereby promoting bioimaging data sharing and reuse. With this update, SSBD is now positioned as a core repository and database within the foundingGIDE, an international consortium working to establish a global image data ecosystem. Harmonizing metadata between data resources enables cross-searching and data exchange with data resources from other countries and regions.

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.

Most Pleiotropic Effects of Gene Knockouts Are Evolutionarily Transient in Yeasts

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects.

Materials evaluation using cell-sized liposomes

Cell membranes play a vital role in delineating the internal cellular environment from the external surroundings, going beyond mere compartmentalization. Researchers have delved into the structural organization, properties, and functional roles of biological membranes, paving the way for their application in substance identification, detection, and quantification. This review introduces various studies and their implications for future research. It underscores the advantages of employing cell-sized liposomes, which enable real-time observation for rapid detection and analysis of diverse materials. The utility of cell-sized liposomes extends to their size, dynamic shape changes, and phase-separation, offering valuable insights into the evaluation of targeted materials.

Rational selection of morphological phenotypic traits to extract essential similarities in chemical perturbation in the ergosterol pathway

Terbinafine, fluconazole, and amorolfine inhibit fungal ergosterol synthesis by acting on their target enzymes at different steps in the synthetic pathway, causing the accumulation of various intermediates. We found that the effects of these three in- hibitors on yeast morphology were different. The number of morphological parameters commonly altered by these drugs was only approximately 6% of the total. Using a rational strategy to find commonly changed parameters,we focused on hidden essential similarities in the phenotypes possibly due to decreased ergosterol levels. This resulted in higher apparent morphological similarity. Improvements in morphological similarity were observed even when canonical correlation analysis was used to select biologically meaningful morphological parameters related to gene function. In addition to changes in cell morphology, we also observed differences in the synergistic effects among the three inhibitors and in their fungicidal effects against pathogenic fungi possibly due to the accumulation of different intermediates. This study provided a comprehensive understanding of the properties of inhibitors acting in the same biosynthetic pathway.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

A Decade of Discovery-Eukaryotic Replisome Disassembly at Replication Termination

The eukaryotic replicative helicase (CMG complex) is assembled during DNA replication initiation in a highly regulated manner, which is described in depth by other manuscripts in this Issue. During DNA replication, the replicative helicase moves through the chromatin, unwinding DNA and facilitating nascent DNA synthesis by polymerases. Once the duplication of a replicon is complete, the CMG helicase and the remaining components of the replisome need to be removed from the chromatin. Research carried out over the last ten years has produced a breakthrough in our understanding, revealing that replication termination, and more specifically replisome disassembly, is indeed a highly regulated process. This review brings together our current understanding of these processes and highlights elements of the mechanism that are conserved or have undergone divergence throughout evolution. Finally, we discuss events beyond the classic termination of DNA replication in S-phase and go over the known mechanisms of replicative helicase removal from chromatin in these particular situations.

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.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae

Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralog TDH2. TDH2 is upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. TDH1, a second and more distantly related paralog of TDH3, has diverged in its regulation and is upregulated by another mechanism. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.

Patterns and evolutionary consequences of pleiotropy

Pleiotropy refers to the phenomenon of one gene or one mutation affecting multiple phenotypic traits. While the concept of pleiotropy is as old as Mendelian genetics, functional genomics has finally allowed the first glimpses of the extent of pleiotropy for a large fraction of genes in a genome. After describing conceptual and operational difficulties in quantifying pleiotropy and the pros and cons of various methods for measuring pleiotropy, I review empirical data on pleiotropy, which generally show an L-shaped distribution of the degree of pleiotropy (i.e., the number of traits affected) with most genes having low pleiotropy. I then review the current understanding of the molecular basis of pleiotropy. The rest of the review discusses evolutionary consequences of pleiotropy, focusing on advances in topics including the cost of complexity, regulatory vs. coding evolution, environmental pleiotropy and adaptation, evolution of ageing and other seemingly harmful traits, and evolutionary resolution of pleiotropy.

Detecting natural selection in trait-trait coevolution

No phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general.

Genetic interactions under the microscope

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

Targeted Mutations Produce Divergent Characteristics in Pedigreed Sake Yeast Strains

Modification of the genetic background and, in some cases, the introduction of targeted mutations can play a critical role in producing trait characteristics during the breeding of crops, livestock, and microorganisms. However, the question of how similar trait characteristics emerge when the same target mutation is introduced into different genetic backgrounds is unclear. In a previous study, we performed genome editing of AWA1, CAR1, MDE1, and FAS2 on the standard sake yeast strain Kyokai No. 7 to breed a sake yeast with multiple excellent brewing characteristics. By introducing the same targeted mutations into other pedigreed sake yeast strains, such as Kyokai strains No. 6, No. 9, and No. 10, we were able to create sake yeasts with the same excellent brewing characteristics. However, we found that other components of sake made by the genome-edited yeast strains did not change in the exact same way. For example, amino acid and isobutanol contents differed among the strain backgrounds. We also showed that changes in yeast cell morphology induced by the targeted mutations also differed depending on the strain backgrounds. The number of commonly changed morphological parameters was limited. Thus, divergent characteristics were produced by the targeted mutations in pedigreed sake yeast strains, suggesting a breeding strategy to generate a variety of sake yeasts with excellent brewing characteristics.

Prediction of ethanol fermentation under stressed conditions using yeast morphological data

A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intelligence (AI) models to predict ethanol yields in yeast fermentation cultures under conditions of high-sugar stress using cell morphological data. Our method involves the extraction of high-dimensional morphological data from phase contrast images using image processing software, and predicting ethanol yields by supervised machine learning. The neural network algorithm produced the best performance, with a coefficient of determination (R²) of 0.95, and could predict ethanol yields well even 60 min in the future. Morphological data from cells cultured in low-glucose medium could not be used for accurate prediction under conditions of high-glucose stress. Cells cultured in high-concentration glucose medium were similar in terms of morphology to cells cultured under high osmotic pressure. Feeding experiments revealed that morphological changes differed depending on the fermentation phase. By monitoring the morphology of yeast under stress, it was possible to understand the intracellular physiological conditions, suggesting that analysis of cell morphology can aid the management and stable production of desired biocommodities.

The pH-sensing Rim101 pathway regulates cell size in budding yeast

Although cell size regulation is crucial for cellular functions in a variety of organisms from bacteria to humans, the underlying mechanisms remain elusive. Here, we identify Rim21, a component of the pH-sensing Rim101 pathway, as a positive regulator of cell size through a flow cytometry-based genome-wide screen of Saccharomyces cerevisiae deletion mutants. We found that mutants defective in the Rim101 pathway were consistently smaller than wildtype cells in the log and stationary phases. We show that the expression of the active form of Rim101 increased the size of wildtype cells. Furthermore, the size of wildtype cells increased in response to external alkalization. Microscopic observation revealed that this cell size increase was associated with changes in both vacuolar and cytoplasmic volume. We also found that these volume changes were dependent on Rim21 and Rim101. In addition, a mutant lacking Vph1, a component of V-ATPase that is transcriptionally regulated by Rim101, was also smaller than wildtype cells, with no increase in size in response to alkalization. We demonstrate that the loss of Vph1 suppressed the Rim101-induced increase in cell size under physiological pH conditions. Taken together, our results suggest that the cell size of budding yeast is regulated by the Rim101-V-ATPase axis under physiological conditions as well as in response to alkaline stresses.

Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae

Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralogs TDH1 and TDH2. TDH1 and TDH2 are upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.

Intelligent sort-timing prediction for image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

Phase Separation in Liposomes Determined by Ergosterol and Classified Using Machine Learning

Recent studies indicated that ergosterol (Erg) helps form strongly ordered lipid domains in membranes that depend on their chemical characters. However, direct evidence of concentration-dependent interaction of Erg with lipid membranes has not been reported. We studied the Erg concentration-dependent changes in the phase behaviors of membranes using cell-sized liposomes containing 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC)/1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC). We observed the concentration range of phase separation in ternary membranes was significantly wider when Erg rather than cholesterol (Chol) was used as the sterol component. We used machine learning for the first time to analyze microscopic images of cell-sized liposomes and identify phase-separated structures. The automated method was successful in identifying homogeneous membranes but performance remained data-limited for the identification of phase separation domains characterized by more complex features.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Gene loss and compensatory evolution promotes the emergence of morphological novelties in budding yeast

Deleterious mutations are generally considered to be irrelevant for morphological evolution. However, they could be compensated by conditionally beneficial mutations, thereby providing access to new adaptive paths. Here we use high-dimensional phenotyping of laboratory-evolved budding yeast lineages to demonstrate that new cellular morphologies emerge exceptionally rapidly as a by-product of gene loss and subsequent compensatory evolution. Unexpectedly, the capacities for invasive growth, multicellular aggregation and biofilm formation also spontaneously evolve in response to gene loss. These multicellular phenotypes can be achieved by diverse mutational routes and without reactivating the canonical regulatory pathways. These ecologically and clinically relevant traits originate as pleiotropic side effects of compensatory evolution and have no obvious utility in the laboratory environment. The extent of morphological diversity in the evolved lineages is comparable to that of natural yeast isolates with diverse genetic backgrounds and lifestyles. Finally, we show that both the initial gene loss and subsequent compensatory mutations contribute to new morphologies, with their synergistic effects underlying specific morphological changes. We conclude that compensatory evolution is a previously unrecognized source of morphological diversity and phenotypic novelties.

Massive expression of cysteine-containing proteins causes abnormal elongation of yeast cells by perturbing the proteasome

The enhanced green fluorescent protein (EGFP) is considered to be a harmless protein because the critical expression level that causes growth defects is higher than that of other proteins. Here, we found that overexpression of EGFP, but not a glycolytic protein Gpm1, triggered the cell elongation phenotype in the budding yeast Saccharomyces cerevisiae. By the morphological analysis of the cell overexpressing fluorescent protein and glycolytic enzyme variants, we revealed that cysteine content was associated with the cell elongation phenotype. The abnormal cell morphology triggered by overexpression of EGFP was also observed in the fission yeast Schizosaccharomyces pombe. Overexpression of cysteine-containing protein was toxic, especially at high-temperature, while the toxicity could be modulated by additional protein characteristics. Investigation of protein aggregate formation, morphological abnormalities in mutants, and transcriptomic changes that occur upon overexpression of EGFP variants suggested that perturbation of the proteasome by the exposed cysteine of the overexpressed protein causes cell elongation. Overexpression of proteins with relatively low folding properties, such as EGFP, was also found to promote the formation of SHOTA (Seventy kDa Heat shock protein-containing, Overexpression-Triggered Aggregates), an intracellular aggregate that incorporates Hsp70/Ssa1, which induces a heat shock response, while it was unrelated to cell elongation. Evolutionary analysis of duplicated genes showed that cysteine toxicity may be an evolutionary bias to exclude cysteine from highly expressed proteins. The overexpression of cysteine-less moxGFP, the least toxic protein revealed in this study, would be a good model system to understand the physiological state of protein burden triggered by ultimate overexpression of harmless proteins.

Phenomics approaches to understand genetic networks and gene function in yeast

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.

Characterizing the gene-environment interaction underlying natural morphological variation in Neurospora crassa conidiophores using high-throughput phenomics and transcriptomics

Neurospora crassa propagates through dissemination of conidia, which develop through specialized structures called conidiophores. Recent work has identified striking variation in conidiophore morphology, using a wild population collection from Louisiana, United States of America to classify 3 distinct phenotypes: Wild-Type, Wrap, and Bulky. Little is known about the impact of these phenotypes on sporulation or germination later in the N. crassa life cycle, or about the genetic variation that underlies them. In this study, we show that conidiophore morphology likely affects colonization capacity of wild N. crassa isolates through both sporulation distance and germination on different carbon sources. We generated and crossed homokaryotic strains belonging to each phenotypic group to more robustly fit a model for and estimate heritability of the complex trait, conidiophore architecture. Our fitted model suggests at least 3 genes and 2 epistatic interactions contribute to conidiophore phenotype, which has an estimated heritability of 0.47. To uncover genes contributing to these phenotypes, we performed RNA-sequencing on mycelia and conidiophores of strains representing each of the 3 phenotypes. Our results show that the Bulky strain had a distinct transcriptional profile from that of Wild-Type and Wrap, exhibiting differential expression patterns in clock-controlled genes (ccgs), the conidiation-specific gene con-6, and genes implicated in metabolism and communication. Combined, these results present novel ecological impacts of and differential gene expression underlying natural conidiophore morphological variation, a complex trait that has not yet been thoroughly explored.

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

Background

Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results.

Results

Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions.

Conclusions

Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01283-6.

Evaluation of In Situ Antiaging Activity in Saccharomyces cerevisiae BY611 Yeast Cells Treated with Polyalthia longifolia Leaf Methanolic Extract (PLME) Using Different Microscopic Approaches: A Morphology-Based Evaluation

Polyalthia longifolia is known for its anti-oxidative properties, which might contribute to the antiaging action. Hence, the current research was conducted to evaluate the antiaging activity of P. longifolia leaf methanolic extract (PLME) in a yeast model based on morphology using microscopic approaches. Saccharomyces cerevisiae BY611 strain yeast cells were treated with 1.00 mg/mL of PLME. The antiaging activity was assessed by determining the replicative lifespan, total lifespan, vacuole morphology by light microscopy, extra-morphology by scanning (SEM), and intra-morphology by transmission (TEM) electron microscopy. The findings demonstrated that PLME treatment significantly accelerated the replicative and total lifespan of the yeast cells. PLME treatment also delays the formation of large apoptotic-like type 3 yeast cell vacuoles. The untreated yeast cells demonstrated aging morphology via SEM analysis, such as shrinking, regional invaginations, and wrinkled cell surface. The TEM analysis revealed the quintessential aging intracellular morphology such as swollen, wrinkled, or damaged vacuole formation of the circular endoplasmic reticulum, a rupture in the nuclear membrane, fragmentation of the nucleus, and complete damaged cytoplasm. Decisively, the present study revealed the vital role of PLME in the induction of antiaging activity in a yeast model using three microscopic approaches—SEM, TEM, and bright-field light microscope.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

Jerveratrum-Type Steroidal Alkaloids Inhibit β-1,6-Glucan Biosynthesis in Fungal Cell Walls

The limited number of available effective agents necessitates the development of new antifungals. We report that jervine, a jerveratrum-type steroidal alkaloid isolated from Veratrum californicum, has antifungal activity. Phenotypic comparisons of cell wall mutants, K1 killer toxin susceptibility testing, and quantification of cell wall components revealed that β-1,6-glucan biosynthesis was significantly inhibited by jervine. Temperature-sensitive mutants defective in essential genes involved in β-1,6-glucan biosynthesis, including BIG1, KEG1, KRE5, KRE9, and ROT1, were hypersensitive to jervine. In contrast, point mutations in KRE6 or its paralog SKN1 produced jervine resistance, suggesting that jervine targets Kre6 and Skn1. Jervine exhibited broad-spectrum antifungal activity and was effective against human-pathogenic fungi, including Candida parapsilosis and Candida krusei. It was also effective against phytopathogenic fungi, including Botrytis cinerea and Puccinia recondita. Jervine exerted a synergistic effect with fluconazole. Therefore, jervine, a jerveratrum-type steroidal alkaloid used in pharmaceutical products, represents a new class of antifungals active against mycoses and plant-pathogenic fungi. IMPORTANCE Non-Candida albicans Candida species (NCAC) are on the rise as a cause of mycosis. Many antifungal drugs are less effective against NCAC, limiting the available therapeutic agents. Here, we report that jervine, a jerveratrum-type steroidal alkaloid, is effective against NCAC and phytopathogenic fungi. Jervine acts on Kre6 and Skn1, which are involved in β-1,6-glucan biosynthesis. The skeleton of jerveratrum-type steroidal alkaloids has been well studied, and more recently, their anticancer properties have been investigated. Therefore, jerveratrum-type alkaloids could potentially be applied as treatments for fungal infections and cancer.

The relative impact of evolving pleiotropy and mutational correlation on trait divergence

Both pleiotropic connectivity and mutational correlations can restrict the decoupling of traits under divergent selection, but it is unknown which is more important in trait evolution. To address this question, we create a model that permits within-population variation in both pleiotropic connectivity and mutational correlation, and compare their relative importance to trait evolution. Specifically, we developed an individual-based stochastic model where mutations can affect whether a locus affects a trait and the extent of mutational correlations in a population. We find that traits can decouple whether there is evolution in pleiotropic connectivity or mutational correlation, but when both can evolve, then evolution in pleiotropic connectivity is more likely to allow for decoupling to occur. The most common genotype found in this case is characterized by having one locus that maintains connectivity to all traits and another that loses connectivity to the traits under stabilizing selection (subfunctionalization). This genotype is favored because it allows the subfunctionalized locus to accumulate greater effect size alleles, contributing to increasingly divergent trait values in the traits under divergent selection without changing the trait values of the other traits (genetic modularization). These results provide evidence that partial subfunctionalization of pleiotropic loci may be a common mechanism of trait decoupling under regimes of corridor selection.

AI-based forecasting of ethanol fermentation using yeast morphological data

Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

Transcriptional and chromatin-based partitioning mechanisms uncouple protein scaling from cell size

Biosynthesis scales with cell size such that protein concentrations generally remain constant as cells grow. As an exception, synthesis of the cell-cycle inhibitor Whi5 "sub-scales" with cell size so that its concentration is lower in larger cells to promote cell-cycle entry. Here, we find that transcriptional control uncouples Whi5 synthesis from cell size, and we identify histones as the major class of sub-scaling transcripts besides WHI5 by screening for similar genes. Histone synthesis is thereby matched to genome content rather than cell size. Such sub-scaling proteins are challenged by asymmetric cell division because proteins are typically partitioned in proportion to newborn cell volume. To avoid this fate, Whi5 uses chromatin-binding to partition similar protein amounts to each newborn cell regardless of cell size. Disrupting both Whi5 synthesis and chromatin-based partitioning weakens G1 size control. Thus, specific transcriptional and partitioning mechanisms determine protein sub-scaling to control cell size.

Categorical Matrix Completion With Active Learning for High-Throughput Screening

The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screening strategy is hardly believed to be optimal and cost-effective. By incorporating artificial intelligence, we design an open-source model based on categorical matrix completion and active machine learning to guide high throughput screening experiments. Specifically, we narrow our scope to the high-throughput screening for chemical compound effects on diverse protein sub-cellular locations. In the proposed model, we believe that exploration is more important than the exploitation in the long-run of high-throughput screening experiment, Therefore, we design several innovations to circumvent the existing limitations. In particular, categorical matrix completion is designed to accurately impute the missing experiments while margin sampling is also implemented for uncertainty estimation. The model is systematically tested on both simulated and real data. The simulation results reflect that our model can be robust to diverse scenarios, while the real data results demonstrate the wet-lab applicability of our model for high-throughput screening experiments. Lastly, we attribute the model success to its exploration ability by revealing the related matrix ranks and distinct experiment coverage comparisons.

Computerized fluorescence microscopy of microbial cells

The upgrading of fluorescence microscopy by the introduction of computer technologies has led to the creation of a new methodology, computerized fluorescence microscopy (CFM). CFM improves subjective visualization and combines it with objective quantitative analysis of the microscopic data. CFM has opened up two fundamentally new opportunities for studying microorganisms. The first is the quantitative measurement of the fluorescence parameters of the targeted fluorophores in association with certain structures of individual cells. The second is the expansion of the boundaries of visualization/resolution of intracellular components beyond the "diffraction limit" of light microscopy into the nanometer range. This enables to obtain unique information about the localization and dynamics of intracellular processes at the molecular level. The purpose of this review is to demonstrate the potential of CFM in the study of fundamental aspects of the structural and functional organization of microbial cells. The basics of computer processing and analysis of digital images are briefly described. The fluorescent molecules used in CFM with an emphasis on fluorescent proteins are characterized. The main methods of super-resolution microscopy (nanoscopy) are presented. The capabilities of various CFM methods for exploring microbial cells at the subcellular level are illustrated by the examples of various studies on yeast and bacteria.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

Defining Functions of Mannoproteins in Saccharomyces cerevisiae by High-Dimensional Morphological Phenotyping

Mannoproteins are non-filamentous glycoproteins localized to the outermost layer of the yeast cell wall. The physiological roles of these structural components have not been completely elucidated due to the limited availability of appropriate tools. As the perturbation of mannoproteins may affect cell morphology, we investigated mannoprotein mutants in Saccharomyces cerevisiae via high-dimensional morphological phenotyping. The mannoprotein mutants were morphologically classified into seven groups using clustering analysis with Gaussian mixture modeling. The pleiotropic phenotypes of cluster I mutant cells (ccw12Δ) indicated that CCW12 plays major roles in cell wall organization. Cluster II (ccw14Δ, flo11Δ, srl1Δ, and tir3Δ) mutants exhibited altered mother cell size and shape. Mutants of cluster III and IV exhibited no or very small morphological defects. Cluster V (dse2Δ, egt2Δ, and sun4Δ) consisted of endoglucanase mutants with cell separation defects due to incomplete septum digestion. The cluster VI mutant cells (ecm33Δ) exhibited perturbation of apical bud growth. Cluster VII mutant cells (sag1Δ) exhibited differences in cell size and actin organization. Biochemical assays further confirmed the observed morphological defects. Further investigations based on various omics data indicated that morphological phenotyping is a complementary tool that can help with gaining a deeper understanding of the functions of mannoproteins.

Virulence Factors and in-Host Selection on Phenotypes in Infectious Probiotic Yeast Isolates (Saccharomyces 'boulardii')

Saccharomyces yeast probiotics (S. ‘boulardii’) have long been applied in the treatment of several gastrointestinal conditions. Despite their widespread use, they are rare opportunistic pathogens responsible for a high proportion of Saccharomyces mycosis cases. The potential virulence attributes of S. ‘boulardii’ as well as its interactions with the human immune system have been studied, however, no information is available on how these yeasts may change due to in-host evolution. To fill this gap, we compared the general phenotypic characteristics, cell morphology, virulence factors, epithelial and immunological interactions, and pathogenicity of four probiotic product samples, two mycosis, and eight non-mycosis samples of S. ‘boulardii’. We assessed the characteristics related to major steps of yeast infections. Mycosis and non-mycosis isolates both displayed novel characters when compared to the product isolates, but in the case of most virulence factors and in pathogenicity, differences were negligible or, surprisingly, the yeasts from products showed elevated levels. No isolates inflicted considerable damage to the epithelial model or bore the hallmarks of immune evasion. Our results show that strains in probiotic products possess characteristics that enable them to act as pathogens upon permissive conditions, and their entry into the bloodstream is not due to active mechanisms but depends on the host. Survival in the host is dependent on yeast phenotypic characteristics which may change in many ways once they start evolving in the host. These facts call attention to the shortcomings of virulence phenotyping in yeast research, and the need for a more thorough assessment of probiotic use.

Profiling the physiological pitfalls of anti-hepatitis C direct-acting agents in budding yeast

Sofosbuvir and Daclatasvir are among the direct-acting antiviral (DAA) medications prescribed for the treatment of chronic hepatitis C (CHC) virus infection as combination therapy with other antiviral medications. DAA-based therapy achieves high cure rates, reaching up to 97% depending on the genotype of the causative hepatitis C virus (HCV). While DAAs have been approved as an efficient and well-tolerated therapy for CHC, emerging concerns about adverse cardiac side effects, higher risk of recurrence and occurrence of hepatocellular carcinoma (HCC) and doubts of genotoxicity have been reported. In our study, we investigated in detail physiological off-targets of DAAs and dissected the effects of these drugs on cellular organelles using budding yeast, a unicellular eukaryotic organism. DAAs were found to disturb the architecture of the endoplasmic reticulum (ER) and the mitochondria, while showing no apparent genotoxicity or DNA damaging effect. Our study provides evidence that DAAs are not associated with genotoxicity and highlights the necessity for adjunctive antioxidant therapy to mitigate the adverse effects of DAAs on ER and mitochondria.

Bidirectional Genetic Control of Phenotypic Heterogeneity and Its Implication for Cancer Drug Resistance

Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or "phenotypic diversifiers," as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity as well as their distinct evolutionary roles and functional independence.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

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.

Polyploid engineering by increasing mutant gene dosage in yeasts

The yeast Saccharomyces cerevisiae, widely used for ethanol production, is one of the best-understood biological systems. Diploid strains of S. cerevisiae are preferred for industrial use due to the better fermentation efficiency, in terms of vitality and endurance as compared to those of haploid strains. Whole-genome duplications is known to promote adaptive mutations in microorganisms, and allelic variations considerably contribute to the product composition in ethanol fermentation. Although fermentation can be regulated using various strains of yeast, it is quite difficult to make fine adjustment of each component in final products. In this study, we demonstrate the use of polyploids with varying gene dosage (the number of copies of a particular gene present in a genome) in the regulation of ethanol fermentation. Ethyl caproate is one of the major flavouring agents in a Japanese alcoholic beverage called sake. A point mutation in FAS2 encoding the α subunit of fatty acid synthetase induces an increase in the amount of caproic acid, a precursor of ethyl caproate. Using the FAS2 as a model, we generated and evaluated yeast strains with varying mutant gene dosage. We demonstrated the possibility to increase mutant gene dosage via loss of heterozygosity in diploid and tetraploid strains. Productivity of ethyl caproate gradually increased with mutant gene dosage among tetraploid strains. This approach can potentially be applied to a variety of yeast strain development via growth-based screening.

Cell volume homeostatically controls the rDNA repeat copy number and rRNA synthesis rate in yeast

The adjustment of transcription and translation rates to the changing needs of cells is of utmost importance for their fitness and survival. We have previously shown that the global transcription rate for RNA polymerase II in budding yeast Saccharomyces cerevisiae is regulated in relation to cell volume. Total mRNA concentration is constant with cell volume since global RNApol II-dependent nascent transcription rate (nTR) also keeps constant but mRNA stability increases with cell size. In this paper, we focus on the case of rRNA and RNA polymerase I. Contrarily to that found for RNA pol II, we detected that RNA polymerase I nTR increases proportionally to genome copies and cell size in polyploid cells. In haploid mutant cells with larger cell sizes, the rDNA repeat copy number rises. By combining mathematical modeling and experimental work with the large-size cln3 strain, we observed that the increasing repeat copy number is based on a feedback mechanism in which Sir2 histone deacetylase homeostatically controls the amplification of rDNA repeats in a volume-dependent manner. This amplification is paralleled with an increase in rRNA nTR, which indicates a control of the RNA pol I synthesis rate by cell volume.

Synthesis rates of biological macromolecules should be strictly regulated and adjusted to the changing conditions of cells. The change in volume is one of the commonest variables along individual cell life and also when comparing different cell types. We previously found that cells with asymmetric division, such as budding yeasts, use a compensatory change in the global RNA polymerase II synthesis rate and mRNA decay rate to maintain mRNA homeostasis. In the present study, we address the same issue for the RNA polymerase that makes rRNAs, which are essential components of ribosomes and the most abundant RNAs in the cell. We found that the copy number of the gene encoding 35S rRNA, transcribed by RNA polymerase I, changes proportionally to the cell volume in budding yeast via a feedback mechanism based on the Sir2 histone deacetylase, which guarantees that yeast cells have the appropriate RNA polymerase I synthesis rate required for rRNA homeostasis.

The Origin of Additive Genetic Variance Driven by Positive Selection

Fisher's fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability (h2) for traits important to fitness. However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity (e.g., humans), the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher's fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.

Protein interaction potential landscapes for yeast replicative aging

We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

Morphological profiling of small molecules

Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.

Coefficient of variation as an image-intensity metric for cytoskeleton bundling

The evaluation of cytoskeletal bundling is a fundamental experimental method in the field of cell biology. Although the skewness of the pixel intensity distribution derived from fluorescently-labeled cytoskeletons has been widely used as a metric to evaluate the degree of bundling in digital microscopy images, its versatility has not been fully validated. Here, we applied the coefficient of variation (CV) of intensity values as an alternative metric, and compared its performance with skewness. In synthetic images representing extremely bundled conditions, the CV successfully detected degrees of bundling that could not be distinguished by skewness. On actual microscopy images, CV was better than skewness, especially on variable-angle epifluorescence microscopic images or stimulated emission depletion and confocal microscopy images of very small areas of around 1 μm2. When blur or noise was added to synthetic images, CV was found to be robust to blur but deleteriously affected by noise, whereas skewness was robust to noise but deleteriously affected by blur. For confocal images, CV and skewness showed similar sensitivity to noise, possibly because optical blurring is often present in microscopy images. Therefore, in practical use with actual microscopy images, CV may be more appropriate than skewness, unless the image is extremely noisy.

Size of Cells and Physicochemical Properties of Membranes are Related to Flavor Production during Sake Brewing in the Yeast Saccharomyces cerevisiae

Ethyl caproate (EC) and isoamyl acetate (IA) are key flavor components of sake. Recently, attempts have been made to increase the content of good flavor components, such as EC and IA, in sake brewing. However, the functions of EC and IA in yeast cells remain poorly understood. Therefore, we investigated the effects of EC and IA using cell-sized lipid vesicles. We also investigated lipid vesicles containing EC and/or caproic acid (CA) as well as IA and/or isoamyl alcohol (IAA). CA and IAA are precursors of EC and IA, respectively, and are important flavors in sake brewing. The size of a vesicle is influenced by flavor compounds and their precursors in a concentration-dependent manner. We aimed to establish the conditions in which the vesicles contained more flavors simultaneously and with different ratios. Interestingly, vesicles were largest in a mixture of 50% of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) with 25% EC and 25% CA or a mixture of 50% DOPC with 25% IA and 25% IAA. The impact of flavor additives on membrane fluidity was also studied using Laurdan generalized polarization. During the production process, flavors may regulate the fluidity of lipid membranes.