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.

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.

BIONIC: biological network integration using convolutions.

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Jerveratrum-Type Steroidal Alkaloids Inhibit ß-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 ß-1,6-glucan biosynthesis was significantly inhibited by jervine. Temperature-sensitive mutants defective in essential genes involved in ß-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 ß-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.

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.

Rhodium-Catalyzed Synthesis of Unsymmetric Di(heteroaryl)ureas Involving an Equilibrium Shift.

Unsymmetric di(heteroaryl)ureas such as HetAr1-NHCONH-HetAr2 are efficiently synthesized from two symmetric ureas, HetAr1-NHCONH-HetAr1 and HetAr2-NHCONH-HetAr2, by rhodium-catalyzed exchange reactions. The equilibrium in some of the reactions can be shifted to the formation of unsymmetric ureas by the aggregation of the dimers formed by inter- and intramolecular hydrogen bonding.

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.

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.

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.

Poacic acid, a ß-1,3-glucan-binding antifungal agent, inhibits cell-wall remodeling and activates transcriptional responses regulated by the cell-wall integrity and high-osmolarity glycerol pathways in yeast.

As a result of the relatively few available antifungals and the increasing frequency of resistance to them, the development of novel antifungals is increasingly important. The plant natural product poacic acid (PA) inhibits ß-1,3-glucan synthesis in Saccharomyces cerevisiae and has antifungal activity against a wide range of plant pathogens. However, the mode of action of PA is unclear. Here, we reveal that PA specifically binds to ß-1,3-glucan, its affinity for which is ~30-fold that for chitin. Besides its effect on ß-1,3-glucan synthase activity, PA inhibited the yeast glucan-elongating activity of Gas1 and Gas2 and the chitin-glucan transglycosylase activity of Crh1. Regarding the cellular response to PA, transcriptional co-regulation was mediated by parallel activation of the cell-wall integrity (CWI) and high-osmolarity glycerol signaling pathways. Despite targeting ß-1,3-glucan remodeling, the transcriptional profiles and regulatory circuits activated by caspofungin, zymolyase, and PA differed, indicating that their effects on CWI have different mechanisms. The effects of PA on the growth of yeast strains indicated that it has a mode of action distinct from that of echinocandins, suggesting it is a unique antifungal agent.

AUTACs: Cargo-Specific Degraders Using Selective Autophagy.

Protein silencing represents an essential tool in biomedical research. Targeted protein degradation (TPD) strategies exemplified by PROTACs are rapidly emerging as modalities in drug discovery. However, the scope of current TPD techniques is limited because many intracellular materials are not substrates of proteasomal clearance. Here, we described a novel targeted-clearance strategy (autophagy-targeting chimera [AUTAC]) that contains a degradation tag (guanine derivatives) and a warhead to provide target specificity. As expected from the substrate scope of autophagy, AUTAC degraded fragmented mitochondria as well as proteins. Mitochondria-targeted AUTAC accelerated both the removal of dysfunctional fragmented mitochondria and the biogenesis of functionally normal mitochondria in patient-derived fibroblast cells. Cytoprotective effects against acute mitochondrial injuries were also seen. Canonical autophagy is viewed as a nonselective bulk decomposition system, and none of the available autophagy-inducing agents exhibit useful cargo selectivity. With its target specificity, AUTAC provides a new modality for research on autophagy-based drugs.

Chemical screening of novel strigolactone agonists that specifically interact with DWARF14 protein.

Strigolactones (SLs) are a class of plant hormones that regulate shoot branching as well as being known as root-derived signals for parasitic and symbiotic interactions. The physical interaction between SLs and the DWARF14 (D14) receptor family can be examined by differential scanning fluorimetry (DSF) that monitors the changes in protein melting temperature (Tm). The Tm of D14 is lowered by bioactive SLs in DSF analysis. In this report, we screened the compounds that lower the Tm of Arabidopsis D14 (AtD14) as potential candidates for SL agonists using DSF analysis. Subsequent physiological analyzes revealed that 113D10 acts as a novel SL agonist in a D14-dependent manner. Intriguingly, 113D10 has a chemical structure different from natural SLs in that it does not possess an enol ether bond that connects to a methylbutenolide moiety. Moreover, 113D10 does not stimulate seed germination of root parasitic plants. Accordingly, 113D10 can be a useful tool for SL studies and agricultural applications.