Yeast as a Tool to Understand the Significance of Human Disease-Associated Gene Variants

Biological autoluminescence enables effective monitoring of yeast cell electroporation

The application of pulsed electric fields (PEFs) is becoming a promising tool for application in biotechnology, and the food industry. However, real-time monitoring of the efficiency of PEF treatment conditions is challenging, especially at the industrial scale and in continuous production conditions.  To overcome this challenge, we have developed a straightforward setup capable of real-time detection of yeast biological autoluminescence (BAL) during pulsing. Saccharomyces cerevisiae culture was exposed to 8 pulses of 100 µs width with electric field strength magnitude 2-7 kV cm-1. To assess the sensitivity of our method in detecting yeast electroporation, we conducted a comparison with established methods including impedance measurements, propidium iodide uptake, cell growth assay, and fluorescence microscopy. Our results demonstrate that yeast electroporation can be instantaneously monitored during pulsing, making it highly suitable for industrial applications. Furthermore, the simplicity of our setup facilitates its integration into continuous liquid flow systems. Additionally, we have established quantitative indicators based on a thorough statistical analysis of the data that can be implemented through a dedicated machine interface, providing efficiency indicators for analysis.

Leveraging yeast sequestration to study and engineer posttranslational modification enzymes

Enzymes that catalyze posttranslational modifications (PTMs) of peptides and proteins (PTM-enzymes)-proteases, protein ligases, oxidoreductases, kinases, and other transferases-are foundational to our understanding of health and disease and empower applications in chemical biology, synthetic biology, and biomedicine. To fully harness the potential of PTM-enzymes, there is a critical need to decipher their enzymatic and biological mechanisms, develop molecules that can probe and modulate them, and endow them with improved and novel functions. These objectives are contingent upon implementation of high-throughput functional screens and selections that interrogate large sequence libraries to isolate desired PTM-enzyme properties. This review discusses the principles of Saccharomyces cerevisiae organelle sequestration to study and engineer PTM-enzymes. These include outer membrane sequestration, specifically methods that modify yeast surface display, and cytoplasmic sequestration based on enzyme-mediated transcription activation. Furthermore, we present a detailed discussion of yeast endoplasmic reticulum sequestration for the first time. Where appropriate, we highlight the major features and limitations of different systems, specifically how they can measure and control enzyme catalytic efficiencies. Taken together, yeast-based high-throughput sequestration approaches significantly lower the barrier to understanding how PTM-enzymes function and how to reprogram them.

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Next-generation sequencing technology has rapidly accelerated the discovery of genetic variants of interest in individuals with rare diseases. However, showing that these variants are causative of the disease in question is complex and may require functional studies. Use of non-mammalian model organisms - mainly fruitflies (Drosophila melanogaster), nematode worms (Caenorhabditis elegans) and zebrafish (Danio rerio) - enables the rapid and cost-effective assessment of the effects of gene variants, which can then be validated in mammalian model organisms such as mice and in human cells. By probing mechanisms of gene action and identifying interacting genes and proteins in vivo, recent studies in these non-mammalian model organisms have facilitated the diagnosis of numerous genetic diseases and have enabled the screening and identification of therapeutic options for patients. Studies in non-mammalian model organisms have also shown that the biological processes underlying rare diseases can provide insight into more common mechanisms of disease and the biological functions of genes. Here, we discuss the opportunities afforded by non-mammalian model organisms, focusing on flies, worms and fish, and provide examples of their use in the diagnosis of rare genetic diseases.

In vivo characterization of the critical interaction between the RNA exosome and the essential RNA helicase Mtr4 in Saccharomyces cerevisiae

The RNA exosome is a conserved molecular machine that processes/degrades numerous coding and non-coding RNAs. The 10-subunit complex is composed of three S1/KH cap subunits (human EXOSC2/3/1; yeast Rrp4/40/Csl4), a lower ring of six PH-like subunits (human EXOSC4/7/8/9/5/6; yeast Rrp41/42/43/45/46/Mtr3), and a singular 3'-5' exo/endonuclease DIS3/Rrp44. Recently, several disease-linked missense mutations have been identified in structural cap and core RNA exosome genes. In this study, we characterize a rare multiple myeloma patient missense mutation that was identified in the cap subunit gene EXOSC2. This missense mutation results in a single amino acid substitution, p.Met40Thr, in a highly conserved domain of EXOSC2. Structural studies suggest that this Met40 residue makes direct contact with the essential RNA helicase, MTR4, and may help stabilize the critical interaction between the RNA exosome complex and this cofactor. To assess this interaction in vivo, we utilized the Saccharomyces cerevisiae system and modeled the EXOSC2 patient mutation into the orthologous yeast gene RRP4, generating the variant rrp4-M68T. The rrp4-M68T cells show accumulation of certain RNA exosome target RNAs and show sensitivity to drugs that impact RNA processing. We also identified robust negative genetic interactions between rrp4-M68T and specific mtr4 mutants. A complementary biochemical approach revealed that Rrp4 M68T shows decreased interaction with Mtr4, consistent with these genetic results. This study suggests that the EXOSC2 mutation identified in a multiple myeloma patient impacts the function of the RNA exosome and provides functional insight into a critical interface between the RNA exosome and Mtr4.

Homo cerevisiae-Leveraging Yeast for Investigating Protein-Protein Interactions and Their Role in Human Disease

Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Yeast as a Model to Find New Drugs and Drug Targets for VPS13-Dependent Neurodegenerative Diseases

Mutations in human VPS13A-D genes result in rare neurological diseases, including chorea-acanthocytosis. The pathogenesis of these diseases is poorly understood, and no effective treatment is available. As VPS13 genes are evolutionarily conserved, the effects of the pathogenic mutations could be studied in model organisms, including yeast, where one VPS13 gene is present. In this review, we summarize advancements obtained using yeast. In recent studies, vps13Δ and vps13-I2749 yeast mutants, which are models of chorea-acanthocytosis, were used to screen for multicopy and chemical suppressors. Two of the suppressors, a fragment of the MYO3 and RCN2 genes, act by downregulating calcineurin activity. In addition, vps13Δ suppression was achieved by using calcineurin inhibitors. The other group of multicopy suppressors were genes: FET4, encoding iron transporter, and CTR1, CTR3 and CCC2, encoding copper transporters. Mechanisms of their suppression rely on causing an increase in the intracellular iron content. Moreover, among the identified chemical suppressors were copper ionophores, which require a functional iron uptake system for activity, and flavonoids, which bind iron. These findings point at areas for further investigation in a higher eukaryotic model of VPS13-related diseases and to new therapeutic targets: calcium signalling and copper and iron homeostasis. Furthermore, the identified drugs are interesting candidates for drug repurposing for these diseases.

Validation and Data-Integration of Yeast-Based Assays for Functional Classification of BRCA1 Missense Variants

Germline mutations in the BRCA1 gene have been reported to increase the lifetime risk of developing breast and/or ovarian cancer (BOC). By new sequencing technologies, numerous variants of uncertain significance (VUS) are identified. It is mandatory to develop new tools to evaluate their functional impact and pathogenicity. As the expression of pathogenic BRCA1 variants in Saccharomyces cerevisiae increases the frequency of intra- and inter-chromosomal homologous recombination (HR), and gene reversion (GR), we validated the two HR and the GR assays by testing 23 benign and 23 pathogenic variants and compared the results with those that were obtained in the small colony phenotype (SCP) assay, an additional yeast-based assay, that was validated previously. We demonstrated that they scored high accuracy, sensitivity, and sensibility. By using a classifier that was based on majority of voting, we have integrated data from HR, GR, and SCP assays and developed a reliable method, named yBRCA1, with high sensitivity to obtain an accurate VUS functional classification (benign or pathogenic). The classification of BRCA1 variants, important for assessing the risk of developing BOC, is often difficult to establish with genetic methods because they occur rarely in the population. This study provides a new tool to get insights on the functional impact of the BRCA1 variants.

Heterologous Expression and Assembly of Human TLR Signaling Components in Saccharomyces cerevisiae

Toll-like receptor (TLR) signaling is key to detect pathogens and initiating inflammation. Ligand recognition triggers the assembly of supramolecular organizing centers (SMOCs) consisting of large complexes composed of multiple subunits. Building such signaling hubs relies on Toll Interleukin-1 Receptor (TIR) and Death Domain (DD) protein-protein interaction domains. We have expressed TIR domain-containing components of the human myddosome (TIRAP and MyD88) and triffosome (TRAM and TRIF) SMOCs in Saccharomyces cerevisiae, as a platform for their study. Interactions between the TLR4 TIR domain, TIRAP, and MyD88 were recapitulated in yeast. Human TIRAP decorated the yeast plasma membrane (PM), except for the bud neck, whereas MyD88 was found at cytoplasmic spots, which were consistent with endoplasmic reticulum (ER)-mitochondria junctions, as evidenced by co-localization with Mmm1 and Mdm34, components of the ER and Mitochondria Encounter Structures (ERMES). The formation of MyD88-TIRAP foci at the yeast PM was reinforced by co-expression of a membrane-bound TLR4 TIR domain. Mutations in essential residues of their TIR domains aborted MyD88 recruitment by TIRAP, but their respective subcellular localizations were unaltered. TRAM and TRIF, however, did not co-localize in yeast. TRAM assembled long PM-bound filaments that were disrupted by co-expression of the TLR4 TIR domain. Our results evidence that the yeast model can be exploited to study the interactions and subcellular localization of human SMOC components in vivo.