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.

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.

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.

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.

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.

Callose Synthesis Suppresses Cell Death Induced by Low-Calcium Conditions in Leaves.

Despite the importance of preventing calcium (Ca) deficiency disorders in agriculture, knowledge of the molecular mechanisms underlying plant adaptations to low-Ca conditions is limited. In this study, we provide evidence for a crucial involvement of callose synthesis in the survival of Arabidopsis (Arabidopsis thaliana) under low-Ca conditions. A mutant sensitive to low-Ca conditions, low calcium sensitive3 (lcs3), exhibited high levels of cell death in emerging leaves and had defects in its expanding true leaves under low-Ca conditions. Further analyses showed that the causal mutation was located in a putative ß-1,3-glucan (callose) synthase gene, GLUCAN SYNTHASE-LIKE10 (GSL10). Yeast complementation assay results showed that GSL10 encodes a functional callose synthase. Ectopic callose significantly accumulated in wild-type plants under low-Ca conditions, but at a low level in lcs3 The low-Ca sensitivity of lcs3 was phenocopied by the application of callose synthase inhibitors in wild-type plants, which resulted in leaf expansion failure, cell death, and reduced ectopic callose levels under low-Ca conditions. Transcriptome analyses showed that the expression of genes related to cell wall and defense responses was altered in both wild-type plants under low-Ca conditions and in lcs3 under normal-Ca conditions, suggesting that GSL10 is required for the alleviation of both cell wall damage and defense responses caused by low Ca levels. These results suggest that callose synthesis is essential for the prevention of cell death under low-Ca conditions and plays a key role in plants' survival strategies under low-Ca conditions.

High-dimensional single-cell phenotyping reveals extensive haploinsufficiency.

Haploinsufficiency, a dominant phenotype caused by a heterozygous loss-of-function mutation, has been rarely observed. However, high-dimensional single-cell phenotyping of yeast morphological characteristics revealed haploinsufficiency phenotypes for more than half of 1,112 essential genes under optimal growth conditions. Additionally, 40% of the essential genes with no obvious phenotype under optimal growth conditions displayed haploinsufficiency under severe growth conditions. Haploinsufficiency was detected more frequently in essential genes than in nonessential genes. Similar haploinsufficiency phenotypes were observed mostly in mutants with heterozygous deletion of functionally related genes, suggesting that haploinsufficiency phenotypes were caused by functional defects of the genes. A global view of the gene network was presented based on the similarities of the haploinsufficiency phenotypes. Our dataset contains rich information regarding essential gene functions, providing evidence that single-cell phenotyping is a powerful approach, even in the heterozygous condition, for analyzing complex biological systems.

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.

Genetic dissection of the signaling pathway required for the cell wall integrity checkpoint.

The cell wall integrity checkpoint monitors synthesis of cell wall materials during the Saccharomyces cerevisiae cell cycle. Upon perturbation of cell wall synthesis, the cell wall integrity checkpoint is activated, downregulating Clb2 transcription. Here, we identified genes involved in this checkpoint by genetic screening of deletion mutants. In addition to the previously identified dynactin complex, the Las17 complex, in particular the Bzz1 and Vrp1 components, plays a role in this checkpoint. We also revealed that the high osmolarity glycerol (HOG) and cell wall integrity mitogen-activated protein kinase (MAPK) signaling pathways are essential for checkpoint function. The defective checkpoint caused by the deficient dynactin and Las17 complexes was rescued by hyperactivation of the cell wall integrity MAPK pathway, but not by the activated form of Hog1, suggesting an order to these signaling pathways. Mutation of Fkh2, a transcription factor important for Clb2 expression, suppressed the checkpoint-defective phenotype of Las17, HOG MAPK and cell wall integrity MAPK mutations. These results provide genetic evidence that signaling from the cell surface regulates the downstream transcriptional machinery to activate the cell wall integrity checkpoint.

Testing the neutral hypothesis of phenotypic evolution.

Although evolution by natural selection is widely regarded as the most important principle of biology, it is unknown whether phenotypic variations within and between species are mostly adaptive or neutral due to the lack of relevant studies of large, unbiased samples of phenotypic traits. Here, we examine 210 yeast morphological traits chosen because of experimental feasibility irrespective of their potential adaptive values. Our analysis is based on the premise that, under neutrality, the rate of phenotypic evolution measured in the unit of mutational size declines as the trait becomes more important to fitness, analogous to the neutral paradigm that functional genes evolve more slowly than functionless pseudogenes. However, we find faster evolution of more important morphological traits within and between species, rejecting the neutral hypothesis. By contrast, an analysis of 3,466 gene expression traits fails to refute neutrality. Thus, at least in yeast, morphological evolution appears largely adaptive, but the same may not apply to other classes of phenotypes. Our neutrality test is applicable to other species, especially genetic model organisms, for which estimations of mutational size and trait importance are relatively straightforward.

Implications of maintenance of mother-bud neck size in diverse vital processes of Saccharomyces cerevisiae.

The mother-bud neck is defined as the boundary between the mother cell and bud in budding microorganisms, wherein sequential morphological events occur throughout the cell cycle. This study was designed to quantitatively investigate the morphology of the mother-bud neck in budding yeast Saccharomyces cerevisiae. Observation of yeast cells with time-lapse microscopy revealed an increase of mother-bud neck size through the cell cycle. After screening of yeast non-essential gene-deletion mutants with the image processing software CalMorph, we comprehensively identified 274 mutants with broader necks during S/G2 phase. Among these yeasts, we extensively analyzed 19 representative deletion mutants with defects in genes annotated to six gene ontology terms (polarisome, actin reorganization, endosomal tethering complex, carboxy-terminal domain protein kinase complex, DNA replication, and maintenance of DNA trinucleotide repeats). The representative broad-necked mutants exhibited calcofluor white sensitivity, suggesting defects in their cell walls. Correlation analysis indicated that maintenance of mother-bud neck size is important for cellular processes such as cell growth, system robustness, and replicative lifespan. We conclude that neck-size maintenance in budding yeast is regulated by numerous genes and has several aspects that are physiologically significant.

Simulated microgravity triggers characteristic morphology and stress response in Saccharomyces cerevisiae.

Reduction of gravity results in changes in gene expression and morphology in the budding yeast Saccharomyces cerevisiae. We studied the genes responsible for the morphological changes induced by simulated microgravity (SMG) using the yeast morphology data. We comprehensively captured the features of the morphological changes in yeast cells cultured in SMG with CalMorph, a high-throughput image-processing system. Statistical analysis revealed that 95 of 501 morphological traits were significantly affected, which included changes in bud direction, the ratio of daughter to mother cell size, the random daughter cell shape, the large mother cell size, bright nuclei in the M phase, and the decrease in angle between two nuclei. We identified downregulated genes that impacted the morphological changes in conditions of SMG by focusing on each of the morphological features individually. Gene Ontology (GO)-enrichment analysis indicated that morphological changes under conditions of SMG were caused by cooperative downregulation of 103 genes annotated to six GO terms, which included cytoplasmic ribonucleoprotein granule, RNA elongation, mitotic cell cycle phase transition, nucleocytoplasmic transport, protein-DNA complex subunit organization, and RNA localization. P-body formation was also promoted under conditions of SMG. These results suggest that cooperative downregulation of multiple genes occurs in conditions of SMG.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

History, lineage and phenotypic differentiation of sake yeast.

Sake yeast was first isolated as a single yeast strain, Saccharomyces sake, during the Meiji era. Yeast strains suitable for sake fermentation were subsequently isolated from sake brewers and distributed as Kyokai yeast strains. Sake yeast strains that produce characteristic flavors have been bred in response to various market demands and individual preferences. Interestingly, both genetic and morphological studies have indicated that sake yeast used during the Meiji era differs from new sake yeasts derived from Kyokai Strain No. 7 lineage. Here, we discuss the history of sake yeast breeding, from the discovery of sake yeast to the present day, to highlight the achievements of great Japanese scientists and engineers.

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.

Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects.

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.

Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae.

BACKGROUND: The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities. RESULTS: We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups. CONCLUSION: Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications.