Genetic profiling of protein burden and nuclear export overload

Overproduction (op) of proteins triggers cellular defects. One of the consequences of overproduction is the protein burden/cost, which is produced by an overloading of the protein synthesis process. However, the physiology of cells under a protein burden is not well characterized. We performed genetic profiling of protein burden by systematic analysis of genetic interactions between GFP-op, surveying both deletion and temperature-sensitive mutants in budding yeast. We also performed genetic profiling in cells with overproduction of triple-GFP (tGFP), and the nuclear export signal-containing tGFP (NES-tGFP). The mutants specifically interacted with GFP-op were suggestive of unexpected connections between actin-related processes like polarization and the protein burden, which was supported by morphological analysis. The tGFP-op interactions suggested that this protein probe overloads the proteasome, whereas those that interacted with NES-tGFP involved genes encoding components of the nuclear export process, providing a resource for further analysis of the protein burden and nuclear export overload

Poacic acid, a β‐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 β-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 β-1,3-glucan, its affinity for which is ~30-fold that for chitin. Besides its effect on β-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 β-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

High-dimensional single-cell phenotyping reveals extensive haploinsufficiency

<div><p>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.</p></div

Distinct roles of cell wall biogenesis in yeast morphogenesis as revealed by multivariate analysis of high-dimensional morphometric data

This article is under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License.The cell wall of budding yeast is a rigid structure composed of multiple components. To thoroughly understand its involvement in morphogenesis, we used the image analysis software CalMorph to quantitatively analyze cell morphology after treatment with drugs that inhibit different processes during cell wall synthesis. Cells treated with cell wall-affecting drugs exhibited broader necks and increased morphological variation. Tunicamycin, which inhibits the initial step of N-glycosylation of cell wall mannoproteins, induced morphologies similar to those of strains defective in α-mannosylation. The chitin synthase inhibitor nikkomycin Z induced morphological changes similar to those of mutants defective in chitin transglycosylase, possibly due to the critical role of chitin in anchoring the β-glucan network. To define the mode of action of echinocandin B, a 1,3-β-glucan synthase inhibitor, we compared the morphology it induced with mutants of Fks1 that contains the catalytic domain for 1,3-β-glucan synthesis. Echinocandin B exerted morphological effects similar to those observed in some fks1 mutants, with defects in cell polarity and reduced glucan synthesis activity, suggesting that echinocandin B affects not only 1,3-β-glucan synthesis, but also another functional domain. Thus our multivariate analyses reveal discrete functions of cell wall components and increase our understanding of the pharmacology of antifungal drugs. © 2014 Authors.This work was supported by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan (21310127 and 24370002 to Y.O.), a grant from the Comisión Interministerial de Ciencia y Tecnología, Spain (BFU2010-18632 to C.R.), and a Public Health Service grant from the National Institute of Allergy and Infectious Diseases (AI-47837 to J.B.K.). H.O. and S.O. were Research Fellows of the Japan Society for the Promotion of Science.Peer Reviewe

A microfluidic device to acquire high-magnification microphotographs of yeast cells

BACKGROUND: Yeast cell morphology was investigated to reveal the molecular mechanisms of cell morphogenesis and to identify key factors of other processes such as cell cycle progression. We recently developed a semi-automatic image processing program called CalMorph, which allows us to quantitatively analyze yeast cell morphology with the 501 parameters as biological traits and uncover statistical relationships between cell morphological phenotypes and genotypes. However, the current semi-automatic method is not suitable for morphological analysis of large-scale yeast mutants for the reliable prediction of gene functions because of its low-throughput especially at the manual image-acquiring process. RESULTS: In this study, we developed a microfluidic chip designed to acquire successive microscopic images of yeast cells suitable for CalMorph image analysis. With the microfluidic chip, the morphology of living cells and morphological changes that occur during the cell cycle were successfully characterized. CONCLUSION: The microfluidic chip enabled us to acquire the images faster than the conventional method. We speculate that the use of microfluidic chip is effective in acquiring images of large-scale for automated analysis of yeast strains

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

Abstract 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

Examples of positive and negative correlations between GO terms.

<p>Green and magenta edges indicate positive and negative phenotypic correlations, respectively. Genes related to cytoplasmic translation, ribosomal large subunit assembly, and proteasome regulatory particles were enriched in groups 17, 3, and 47, respectively (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005130#pbio.2005130.s009" target="_blank">S8A Table</a>). GO, gene ontology.</p

Global view of functional relationships between haploinsufficient genes.

<p>(A) Graphical representation of haploinsufficient gene network. White and black edges indicate positive and negative phenotypic correlation. Transparency of edges indicates absolute value of the correlation coefficient. Colored nodes represent 285 haploinsufficient genes belonging to core gene groups. (B) Heat map of the phenotypic correlation coefficient between each pair of functional gene groups. Pairs with significant phenotypic correlation coefficients are indicated with colors in the heat map. The upper bar plot indicates the number of pairs with significant similarity. The 15 identified core functional gene groups (black bars) are shown by the representative GO term on the right side. The dendrogram is constructed based on the proportion of significant correlation. GO, gene ontology.</p