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

Machine Learning-Assisted SERS Reveals the Biochemical Signature of Enhanced Protein Secretion from Surface-Modified Magnetic Nanoparticles

This study introduces a novel investigation of the interaction between Komagataella phaffii cells and iron oxide-based magnetic nanoparticles (Fe3O4 MNPs) via protein secretion and machine learning (ML)-assisted surface-enhanced Raman scattering (SERS). For the first time, we produced Fe3O4, Fe3O4@PEG, Fe3O4@PEI10kDa, and Fe3O4@PEI25kDa MNPs by a one-pot coprecipitation reaction. The addition of polymers to the reaction conditions significantly affected the shape, surface charge, size, and size distribution of the MNPs. The surface modification of MNPs is effectively accomplished using polyethylenimine (PEI), and the ζ-potential values of the MNPs exceed +25 mV under the NH4OH control. The homogeneity of MNPs synthesized with NH4OH is more pronounced according to transmission electron microscopy (TEM) pictures. All MNPs exhibited excellent immobilization efficiency (>92%) when we used 250 ppm Fe-containing MNP solutions. Smaller MNPs uniformly encapsulated the surface of K. phaffii cells, whereas larger MNPs exhibited irregular accumulation. K. phaffii cells exhibited excellent viability in all MNP solutions at up to 1000 ppm of Fe concentrations. Finally, the highest recombinant azurin protein secretion rate was obtained in Fe3O4@PEI10kDa MNP-immobilized cells (about 1.3 times). The ML-assisted SERS analysis revealed that MNP interactions with K. phaffii cells were mediated by proteins such as mannoproteins and membrane transporter proteins as well as N-acetylglucosamine (i.e., chitin). These findings revealed the effect of the size and surface properties of MNPs on the immobilization of K. phaffii cells and the enormous potential of magnetic immobilization for protein secretion.

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.

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.

A peroxidase-derived ligand that induces Fusarium graminearum Ste2 receptor-dependent chemotropism

Introduction

The fungal G protein-coupled receptors Ste2 and Ste3 are vital in mediating directional hyphal growth of the agricultural pathogen Fusarium graminearum towards wheat plants. This chemotropism is induced by a catalytic product of peroxidases secreted by the wheat. Currently, the identity of this product, and the substrate it is generated from, are not known.

Methods and results

We provide evidence that a peroxidase substrate is derived from F. graminearum conidia and report a simple method to extract and purify the FgSte2-activating ligand for analyses by mass spectrometry. The mass spectra arising from t he ligand extract are characteristic of a 400 Da carbohydrate moiety. Consistent with this type of molecule, glycosidase treatment of F. graminearum conidia prior to peroxidase treatment significantly reduced the amount of ligand extracted. Interestingly, availability of the peroxidase substrate appears to depend on the presence of both FgSte2 and FgSte3, as knockout of one or the other reduces the chemotropism-inducing effect of the extracts.

Conclusions

While further characterization is necessary, identification of the F. graminearum-derived peroxidase substrate and the FgSte2-activating ligand will unearth deeper insights into the intricate mechanisms that underlie fungal pathogenesis in cereal crops, unveiling novel avenues for inhibitory interventions.

Bni5 tethers myosin-II to septins to enhance retrograde actin flow and the robustness of cytokinesis

The collaboration between septins and myosin-II in driving processes outside of cytokinesis remains largely uncharted. Here, we demonstrate that Bni5 in the budding yeast S. cerevisiae interacts with myosin-II, septin filaments, and the septin-associated kinase Elm1 via distinct domains at its N- and C-termini, thereby tethering the mobile myosin-II to the stable septin hourglass at the division site from bud emergence to the onset of cytokinesis. The septin and Elm1-binding domains, together with a central disordered region, of Bni5 control timely remodeling of the septin hourglass into a double ring, enabling the actomyosin ring constriction. The Bni5-tethered myosin-II enhances retrograde actin cable flow, which contributes to the asymmetric inheritance of mitochondria-associated protein aggregates during cell division, and also strengthens cytokinesis against various perturbations. Thus, we have established a biochemical pathway involving septin-Bni5-myosin-II interactions at the division site, which can inform mechanistic understanding of the role of myosin-II in other retrograde flow systems.

Immunoinformatic-guided designing and evaluating protein and mRNA-based vaccines against Cryptococcus neoformans for immunocompromised patients

BACKGROUND

Cryptococcus neoformans is a fungal pathogen that can cause serious meningoencephalitis in individuals with compromised immune systems due to HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome), liver cirrhosis, and transplantation. Mannoproteins (MPs), glycoproteins in the C. neoformans capsule, crucially impact virulence by mediating adhesion to lung cells and modulating immune response via cytokine induction and phagocytosis influence. Therefore, creating a vaccine that can generate targeted antibodies to fight infection and prevent fungal illnesses is essential.

RESULTS

This research aims to create a unique, stable, and safe vaccine through bioinformatics methodologies, aiming at epitopes of T and B cells found in the MP of C. neoformans. Based on toxicity, immunogenicity, and antigenicity, this research predicted novel T cells (GNPVGGNVT, NPVGGNVTT, QTSYARLLS, TSVGNGIAS, WVMPGDYTN, AAATGSSSSGSTGSG, GSTGSGSGSAAAGST, SGSTGSGSGSAAAGS, SSGSTGSGSGSAAAG, and SSSGSTGSGSGSAAA) and B cell (ANGSTSTFQQRYTGTYTNGDGSLGTWTQGETVTPQTAYSTPATSNCKTYTSVGNGIASLALSNAGSNSTAAATNSSSGGASAAATGSSSSGSTGSGSGSAAAGSTAAASSSGDSSSSTSAAMSNGI, HGATGLGNPVGGNVTT, TMGPTNPSEPTLGTAI, GNPVGGNVTTNATGSD, and NSTAAATNSSSGGASA) epitopes for a multiple-epitope vaccine and constructed a vaccine subunit with potential immunogenic properties. The present study used four linkers (AAY, GPGPG, KK, and EAAAK linkers) to connect the epitopes and adjuvant. After constructing the vaccine, it was confronted with receptor docking and simulation analysis. Subsequently, the vaccine was cloned into the vector of Escherichia coli pET-28a ( +) by ligation process for the expression using the SnapGene tool, which confirmed a significant immune response. To assess the constructed vaccine's properties, multiple computational tools were employed. Based on the MP sequence, the tools evaluated the antigenicity, immunogenicity, cytokine-inducing capacity, allergenicity, toxicity, population coverage, and solubility.

CONCLUSION

Eventually, the results revealed a promising multi-epitope vaccine as a potential candidate for addressing global C. neoformans infection, particularly in immunocompromised patients. Yet, additional in vitro and in vivo investigations are necessary to validate its safety and effectiveness.

Special Issue “The Fungal Cell Wall Integrity Pathway”

Adaptation to external changes is necessary for all cell types to survive and thrive in diverse environments [...]

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.