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

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.

Anomaly detection for high-content image-based phenotypic cell profiling

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.

Poacic Acid, a Plant-Derived Stilbenoid, Augments Cell Wall Chitin Production, but Its Antifungal Activity Is Hindered by This Polysaccharide and by Fungal Essential Metals

Climate and environmental changes have modified the habitats of fungal pathogens, inflicting devastating effects on livestock and crop production. Additionally, drug-resistant fungi are increasing worldwide, driving the urgent need to identify new molecular scaffolds for the development of antifungal agents for humans, animals, and plants. Poacic acid (PA), a plant-derived stilbenoid, was recently discovered to be a novel molecular scaffold that inhibits the growth of several fungi. Its antifungal activity has been associated with perturbation of the production/assembly of the fungal cell wall β-1,3-glucan, but its mode of action is not resolved. In this study, we investigated the antifungal activity of PA and its derivatives on a panel of yeast. PA had a fungistatic effect on S. cerevisiae and a fungicidal effect on plasma membrane-damaged Candida albicans mutants. Live cell fluorescence microscopy experiments revealed that PA increases chitin production and modifies its cell wall distribution. Chitin production and cell growth returned to normal after prolonged incubation. The antifungal activity of PA was reduced in the presence of exogenous chitin, suggesting that the potentiation of chitin production is a stress response that helps the yeast cell overcome the effect of this antifungal stilbenoid. Growth inhibition was also reduced by metal ions, indicating that PA affects the metal homeostasis. These findings suggest that PA has a complex antifungal mechanism of action that involves perturbation of the cell wall β-1,3-glucan production/assembly, chitin production, and metal homeostasis.

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.

QATS: an ImageJ plugin for the quantification of toroidal nuclei in biological images

MOTIVATION

The toroidal nucleus is a novel chromosomal instability (CIN) biomarker which complements the micronucleus. Understanding the specific biological stresses leading to the formation of each CIN-associated phenotype requires the evaluation of large panels of biological images collected from different genetic backgrounds and environmental conditions. However, the quantification of toroidal nuclei is currently a manual process which is unviable on a large scale.

RESULTS

Here, we present QATS (QuAntification of Toroidal nuclei in biological imageS), a tool that automates the identification of toroidal nuclei, minimizing false positives while highly agreeing with the manual quantifications. Additionally, QATS identifies micronuclei for a convenient comparison of both CIN biomarkers. QATS is an open-source ImageJ plugin with a user-friendly interface that enables a wide scientific community to easily assess the frequency of CIN biomarkers for the determination of CIN levels in cellular models.

AVAILABILITY AND IMPLEMENTATION

QATS is an ImageJ plugin freely available at http://www.toroidalnucleus.org/qats. The user manual and the images used for the evaluation of QATS are included in the website. Supplementary data are available at Bioinformatics online.

Deciphering the Biological Activities of Antifungal Agents with Chemical Probes

The growing number of fungal infections caused by pathogens resistant to one or more classes of antifungal drugs emphasizes the threat that these microorganisms pose to animal and human health and global food security. Open questions remain regarding the mechanisms of action of the limited repertoire of antifungal agents, making it challenging to rationally develop more efficacious therapeutics. In recent years, the use of chemical biology approaches has resolved some of these questions and has provided new promising concepts to guide the design of antifungal agents. By focusing on examples from studies carried out in recent years, this minireview describes the key roles that probes based on antifungal agents and their derivatives have played in uncovering details about their activities, in detecting resistance, and in characterizing the interactions between these agents and their targets.