Humanized yeast to model human biology, disease and evolution

Genetic conflicts in budding yeast: The 2μ plasmid as a model selfish element

Antagonistic coevolution, arising from genetic conflict, can drive rapid evolution and biological innovation. Conflict can arise both between organisms and within genomes. This review focuses on budding yeasts as a model system for exploring intra- and inter-genomic genetic conflict, highlighting in particular the 2-micron (2μ) plasmid as a model selfish element. The 2μ is found widely in laboratory strains and industrial isolates of Saccharomyces cerevisiae and has long been known to cause host fitness defects. Nevertheless, the plasmid is frequently ignored in the context of genetic, fitness, and evolution studies. Here, I make a case for further exploring the evolutionary impact of the 2μ plasmid as well as other selfish elements of budding yeasts, discuss recent advances, and, finally, future directions for the field.

A Humanized Yeast Model for Studying TRAPP Complex Mutations; Proof-of-Concept Using Variants from an Individual with a TRAPPC1-Associated Neurodevelopmental Syndrome

Variants in membrane trafficking proteins are known to cause rare disorders with severe symptoms. The highly conserved transport protein particle (TRAPP) complexes are key membrane trafficking regulators that are also involved in autophagy. Pathogenic genetic variants in specific TRAPP subunits are linked to neurological disorders, muscular dystrophies, and skeletal dysplasias. Characterizing these variants and their phenotypes is important for understanding the general and specialized roles of TRAPP subunits as well as for patient diagnosis. Patient-derived cells are not always available, which poses a limitation for the study of these diseases. Therefore, other systems, like the yeast Saccharomyces cerevisiae, can be used to dissect the mechanisms at the intracellular level underlying these disorders. The development of CRISPR/Cas9 technology in yeast has enabled a scar-less editing method that creates an efficient humanized yeast model. In this study, core yeast subunits were humanized by replacing them with their human orthologs, and TRAPPC1, TRAPPC2, TRAPPC2L, TRAPPC6A, and TRAPPC6B were found to successfully replace their yeast counterparts. This system was used for studying the first reported individual with an autosomal recessive disorder caused by biallelic TRAPPC1 variants, a girl with a severe neurodevelopmental disorder and myopathy. We show that the maternal variant (TRAPPC1 p.(Val121Alafs*3)) is non-functional while the paternal variant (TRAPPC1 p.(His22_Lys24del)) is conditional-lethal and affects secretion and non-selective autophagy in yeast. This parallels defects seen in fibroblasts derived from this individual which also showed membrane trafficking defects and altered Golgi morphology, all of which were rescued in the human system by wild-type TRAPPC1. This study suggests that humanized yeast can be an efficient means to study TRAPP subunit variants in the absence of human cells and can assign significance to variants of unknown significance (VUS). This study lays the foundation for characterizing further TRAPP variants through this system, rapidly contributing to disease diagnosis.

Integrating microfluidics and synthetic biology: advancements and diverse applications across organisms

Synthetic biology is the design and modification of biological systems for specific functions, integrating several disciplines like engineering, genetics, and computer science. The field of synthetic biology is to understand biological processes within host organisms through the manipulation and regulation of their genetic pathways and the addition of biocontrol circuits to enhance their production capabilities. This pursuit serves to address global challenges spanning diverse domains that are difficult to tackle through conventional routes of production. Despite its impact, achieving precise, dynamic, and high-throughput manipulation of biological processes is still challenging. Microfluidics offers a solution to those challenges, enabling controlled fluid handling at the microscale, offering lower reagent consumption, faster analysis of biochemical reactions, automation, and high throughput screening. In this review, we diverge from conventional focus on automating the synthetic biology design-build-test-learn cycle, and instead, focus on microfluidic platforms and their role in advancing synthetic biology through its integration with host organisms - bacterial cells, yeast, fungi, animal cells - and cell-free systems. The review illustrates how microfluidic devices have been instrumental in understanding biological systems by showcasing microfluidics as an essential tool to create synthetic genetic circuits, pathways, and organisms within controlled environments. In conclusion, we show how microfluidics expedite synthetic biology applications across diverse domains including but not limited to personalized medicine, bioenergy, and agriculture.

More than Just Bread and Wine: Using Yeast to Understand Inherited Cytochrome Oxidase Deficiencies in Humans

Inherited defects in cytochrome c oxidase (COX) are associated with a substantial subset of diseases adversely affecting the structure and function of the mitochondrial respiratory chain. This multi-subunit enzyme consists of 14 subunits and numerous cofactors, and it requires the function of some 30 proteins to assemble. COX assembly was first shown to be the primary defect in the majority of COX deficiencies 36 years ago. Over the last three decades, most COX assembly genes have been identified in the yeast Saccharomyces cerevisiae, and studies in yeast have proven instrumental in testing the impact of mutations identified in patients with a specific COX deficiency. The advent of accessible genome-wide sequencing capabilities has led to more patient mutations being identified, with the subsequent identification of several new COX assembly factors. However, the lack of genotype-phenotype correlations and the large number of genes involved in generating a functional COX mean that functional studies must be undertaken to assign a genetic variant as being causal. In this review, we provide a brief overview of the use of yeast as a model system and briefly compare the COX assembly process in yeast and humans. We focus primarily on the studies in yeast that have allowed us to both identify new COX assembly factors and to demonstrate the pathogenicity of a subset of the mutations that have been identified in patients with inherited defects in COX. We conclude with an overview of the areas in which studies in yeast are likely to continue to contribute to progress in understanding disease arising from inherited COX deficiencies.

Predictive modeling provides insight into the clinical heterogeneity associated with TARS1 loss-of-function mutations

Aminoacyl-tRNA synthetases (ARSs) are ubiquitously expressed, essential enzymes that complete the first step of protein translation: ligation of amino acids to cognate tRNAs. Genes encoding ARSs have been implicated in myriad dominant and recessive phenotypes, the latter often affecting multiple tissues but with frequent involvement of the central and peripheral nervous system, liver, and lungs. Threonyl-tRNA synthetase (TARS1) encodes the enzyme that ligates threonine to tRNATHR in the cytoplasm. To date, TARS1 variants have been implicated in a recessive brittle hair phenotype. To better understand TARS1-related recessive phenotypes, we engineered three TARS1 missense mutations predicted to cause a loss-of-function effect and studied these variants in yeast and worm models. This revealed two loss-of-function mutations, including one hypomorphic allele (R433H). We next used R433H to study the effects of partial loss of TARS1 function in a compound heterozygous mouse model (R433H/null). This model presents with phenotypes reminiscent of patients with TARS1 variants and with distinct lung and skin defects. This study expands the potential clinical heterogeneity of TARS1-related recessive disease, which should guide future clinical and genetic evaluations of patient populations.

EPI-SF: essential protein identification in protein interaction networks using sequence features

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Systematic profiling of ale yeast protein dynamics across fermentation and repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Humanization reveals pervasive incompatibility of yeast and human kinetochore components

Kinetochores assemble on centromeres to drive chromosome segregation in eukaryotic cells. Humans and budding yeast share most of the structural subunits of the kinetochore, whereas protein sequences have diverged considerably. The conserved centromeric histone H3 variant, CenH3 (CENP-A in humans and Cse4 in budding yeast), marks the site for kinetochore assembly in most species. A previous effort to complement Cse4 in yeast with human CENP-A was unsuccessful; however, co-complementation with the human core nucleosome was not attempted. Previously, our lab successfully humanized the core nucleosome in yeast; however, this severely affected cellular growth. We hypothesized that yeast Cse4 is incompatible with humanized nucleosomes and that the kinetochore represented a limiting factor for efficient histone humanization. Thus, we argued that including the human CENP-A or a Cse4-CENP-A chimera might improve histone humanization and facilitate kinetochore function in humanized yeast. The opposite was true: CENP-A expression reduced histone humanization efficiency, was toxic to yeast, and disrupted cell cycle progression and kinetochore function in wild-type (WT) cells. Suppressors of CENP-A toxicity included gene deletions of subunits of 3 conserved chromatin remodeling complexes, highlighting their role in CenH3 chromatin positioning. Finally, we attempted to complement the subunits of the NDC80 kinetochore complex, individually and in combination, without success, in contrast to a previous study indicating complementation by the human NDC80/HEC1 gene. Our results suggest that limited protein sequence similarity between yeast and human components in this very complex structure leads to failure of complementation.

Saccharomyces cerevisiae as a Model for Studying Human Neurodegenerative Disorders: Viral Capsid Protein Expression

In this article, we briefly describe human neurodegenerative diseases (NDs) and the experimental models used to study them. The main focus is the yeast Saccharomyces cerevisiae as an experimental model used to study neurodegenerative processes. We review recent experimental data on the aggregation of human neurodegenerative disease-related proteins in yeast cells. In addition, we describe the results of studies that were designed to investigate the molecular mechanisms that underlie the aggregation of reporter proteins. The advantages and disadvantages of the experimental approaches that are currently used to study the formation of protein aggregates are described. Special attention is given to the similarity between aggregates that form as a result of protein misfolding and viral factories-special structural formations in which viral particles are formed inside virus-infected cells. A separate part of the review is devoted to our previously published study on the formation of aggregates upon expression of the insect densovirus capsid protein in yeast cells. Based on the reviewed results of studies on NDs and related protein aggregation, as well as viral protein aggregation, a new experimental model system for the study of human NDs is proposed. The core of the proposed system is a comparative transcriptomic analysis of changes in signaling pathways during the expression of viral capsid proteins in yeast cells.

The hide-and-seek game of the oncogenic Epstein-Barr virus-encoded EBNA1 protein with the immune system: An RNA G-quadruplex tale

The Epstein-Barr virus (EBV) is the first oncogenic virus described in human. EBV infects more than 90% of the human population worldwide, but most EBV infections are asymptomatic. After the primary infection, the virus persists lifelong in the memory B cells of the infected individuals. Under certain conditions the virus can cause several human cancers, that include lymphoproliferative disorders such as Burkitt and Hodgkin lymphomas and non-lymphoid malignancies such as 100% of nasopharyngeal carcinoma and 10% of gastric cancers. Each year, about 200,000 EBV-related cancers emerge, hence accounting for at least 1% of worldwide cancers. Like all gammaherpesviruses, EBV has evolved a strategy to escape the host immune system. This strategy is mainly based on the tight control of the expression of its Epstein-Barr nuclear antigen-1 (EBNA1) protein, the EBV-encoded genome maintenance protein. Indeed, EBNA1 is essential for viral genome replication and maintenance but, at the same time, is also highly antigenic and T cells raised against EBNA1 exist in infected individuals. For this reason, EBNA1 is considered as the Achilles heel of EBV and the virus has seemingly evolved a strategy that employs the binding of nucleolin, a host cell factor, to RNA G-quadruplex (rG4) within EBNA1 mRNA to limit its expression to the minimal level required for function while minimizing immune recognition. This review recapitulates in a historical way the knowledge accumulated on EBNA1 immune evasion and discusses how this rG4-dependent mechanism can be exploited as an intervention point to unveil EBV-related cancers to the immune system.

Systematic Profiling of Ale Yeast Protein Dynamics across Fermentation and Repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is amongst the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout two fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Identification and characterization of protein interactions with the major Niemann-Pick type C disease protein in yeast reveals pathways of therapeutic potential

Niemann-Pick type C (NP-C) disease is a rare lysosomal storage disease caused by mutations in NPC1 (95% cases) or NPC2 (5% cases). These proteins function together in cholesterol egress from the lysosome, whereby upon mutation, cholesterol and other lipids accumulate causing major pathologies. However, it is not fully understood how cholesterol is transported from NPC1 residing at the lysosomal membrane to the endoplasmic reticulum (ER) and plasma membrane. The yeast ortholog of NPC1, Niemann-Pick type C-related protein-1 (Ncr1), functions similarly to NPC1; when transfected into a mammalian cell lacking NPC1, Ncr1 rescues the diagnostic hallmarks of cholesterol and sphingolipid accumulation. Here, we aimed to identify and characterize protein-protein interactions (PPIs) with the yeast Ncr1 protein. A genome-wide split-ubiquitin membrane yeast two-hybrid (MYTH) protein interaction screen identified 11 ER membrane-localized, full-length proteins interacting with Ncr1 at the lysosomal/vacuolar membrane. These highlight the importance of ER-vacuole membrane interface and include PPIs with the Cyb5/Cbr1 electron transfer system, the ceramide synthase complex, and the Sec61/Sbh1 protein translocation complex. These PPIs were not detected in a sterol auxotrophy condition and thus depend on normal sterol metabolism. To provide biological context for the Ncr1-Cyb5 PPI, a yeast strain lacking this PPI (via gene deletions) exhibited altered levels of sterols and sphingolipids including increased levels of glucosylceramide that mimic NP-C disease. Overall, the results herein provide new physical and genetic interaction models to further use the yeast model of NP-C disease to better understand human NP-C disease.

Species-specific protein-protein interactions govern the humanization of the 20S proteasome in yeast

Yeast and humans share thousands of genes despite a billion years of evolutionary divergence. While many human genes can functionally replace their yeast counterparts, nearly half of the tested shared genes cannot. For example, most yeast proteasome subunits are "humanizable," except subunits comprising the β-ring core, including β2c (HsPSMB7, a constitutive proteasome subunit). We developed a high-throughput pipeline to humanize yeast proteasomes by generating a large library of Hsβ2c mutants and screening them for complementation of a yeast β2 (ScPup1) knockout. Variants capable of replacing ScPup1 included (1) those impacting local protein-protein interactions (PPIs), with most affecting interactions between the β2c C-terminal tail and the adjacent β3 subunit, and (2) those affecting β2c proteolytic activity. Exchanging the full-length tail of human β2c with that of ScPup1 enabled complementation. Moreover, wild-type human β2c could replace yeast β2 if human β3 was also provided. Unexpectedly, yeast proteasomes bearing a catalytically inactive HsPSMB7-T44A variant that blocked precursor autoprocessing were viable, suggesting an intact propeptide stabilizes late assembly intermediates. In contrast, similar modifications in human β2i (HsPSMB10), an immunoproteasome subunit and the co-ortholog of yeast β2, do not enable complementation in yeast, suggesting distinct interactions are involved in human immunoproteasome core assembly. Broadly, our data reveal roles for specific PPIs governing functional replaceability across vast evolutionary distances.

Supervised biological network alignment with graph neural networks

MOTIVATION

Despite the advances in sequencing technology, massive proteins with known sequences remain functionally unannotated. Biological network alignment (NA), which aims to find the node correspondence between species' protein-protein interaction (PPI) networks, has been a popular strategy to uncover missing annotations by transferring functional knowledge across species. Traditional NA methods assumed that topologically similar proteins in PPIs are functionally similar. However, it was recently reported that functionally unrelated proteins can be as topologically similar as functionally related pairs, and a new data-driven or supervised NA paradigm has been proposed, which uses protein function data to discern which topological features correspond to functional relatedness.

RESULTS

Here, we propose GraNA, a deep learning framework for the supervised NA paradigm for the pairwise NA problem. Employing graph neural networks, GraNA utilizes within-network interactions and across-network anchor links for learning protein representations and predicting functional correspondence between across-species proteins. A major strength of GraNA is its flexibility to integrate multi-faceted non-functional relationship data, such as sequence similarity and ortholog relationships, as anchor links to guide the mapping of functionally related proteins across species. Evaluating GraNA on a benchmark dataset composed of several NA tasks between different pairs of species, we observed that GraNA accurately predicted the functional relatedness of proteins and robustly transferred functional annotations across species, outperforming a number of existing NA methods. When applied to a case study on a humanized yeast network, GraNA also successfully discovered functionally replaceable human-yeast protein pairs that were documented in previous studies.

AVAILABILITY AND IMPLEMENTATION

The code of GraNA is available at https://github.com/luo-group/GraNA.

A humanized yeast model reveals dominant-negative properties of neuropathy-associated alanyl-tRNA synthetase mutations

Aminoacyl-tRNA synthetases (ARSs) are essential enzymes that ligate tRNA molecules to cognate amino acids. Heterozygosity for missense variants or small in-frame deletions in six ARS genes causes dominant axonal peripheral neuropathy. These pathogenic variants reduce enzyme activity without significantly decreasing protein levels and reside in genes encoding homo-dimeric enzymes. These observations raise the possibility that neuropathy-associated ARS variants exert a dominant-negative effect, reducing overall ARS activity below a threshold required for peripheral nerve function. To test such variants for dominant-negative properties, we developed a humanized yeast assay to co-express pathogenic human alanyl-tRNA synthetase (AARS1) mutations with wild-type human AARS1. We show that multiple loss-of-function AARS1 mutations impair yeast growth through an interaction with wild-type AARS1, but that reducing this interaction rescues yeast growth. This suggests that neuropathy-associated AARS1 variants exert a dominant-negative effect, which supports a common, loss-of-function mechanism for ARS-mediated dominant peripheral neuropathy.

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.

Rapid, scalable, combinatorial genome engineering by marker-less enrichment and recombination of genetically engineered loci in yeast

A major challenge to rationally building multi-gene processes in yeast arises due to the combinatorics of combining all of the individual edits into the same strain. Here, we present a precise and multi-site genome editing approach that combines all edits without selection markers using CRISPR-Cas9. We demonstrate a highly efficient gene drive that selectively eliminates specific loci by integrating CRISPR-Cas9-mediated double-strand break (DSB) generation and homology-directed recombination with yeast sexual assortment. The method enables marker-less enrichment and recombination of genetically engineered loci (MERGE). We show that MERGE converts single heterologous loci to homozygous loci at ∼100% efficiency, independent of chromosomal location. Furthermore, MERGE is equally efficient at converting and combining multiple loci, thus identifying compatible genotypes. Finally, we establish MERGE proficiency by engineering a fungal carotenoid biosynthesis pathway and most of the human α-proteasome core into yeast. Therefore, MERGE lays the foundation for scalable, combinatorial genome editing in yeast.

Human gasdermin D and MLKL disrupt mitochondria, endocytic traffic and TORC1 signalling in budding yeast

Gasdermin D (GSDMD) and mixed lineage kinase domain-like protein (MLKL) are the pore-forming effectors of pyroptosis and necroptosis, respectively, with the capacity to disturb plasma membrane selective permeability and induce regulated cell death. The budding yeast Saccharomyces cerevisiae has long been used as a simple eukaryotic model for the study of proteins associated with human diseases by heterologous expression. In this work, we expressed in yeast both GSDMD and its N-terminal domain (GSDMD(NT)) to characterize their cellular effects and compare them to those of MLKL. GSDMD(NT) and MLKL inhibited yeast growth, formed cytoplasmic aggregates and fragmented mitochondria. Loss-of-function point mutants of GSDMD(NT) showed affinity for this organelle. Besides, GSDMD(NT) and MLKL caused an irreversible cell cycle arrest through TORC1 inhibition and disrupted endosomal and autophagic vesicular traffic. Our results provide a basis for a humanized yeast platform to study GSDMD and MLKL, a useful tool for structure-function assays and drug discovery.

Thanksgiving to Yeast, the HMGB Proteins History from Yeast to Cancer

Yeasts have been a part of human life since ancient times in the fermentation of many natural products used for food. In addition, in the 20th century, they became powerful tools to elucidate the functions of eukaryotic cells as soon as the techniques of molecular biology developed. Our molecular understandings of metabolism, cellular transport, DNA repair, gene expression and regulation, and the cell division cycle have all been obtained through biochemistry and genetic analysis using different yeasts. In this review, we summarize the role that yeasts have had in biological discoveries, the use of yeasts as biological tools, as well as past and on-going research projects on HMGB proteins along the way from yeast to cancer.

Pathogenic variants of the coenzyme A biosynthesis-associated enzyme phosphopantothenoylcysteine decarboxylase cause autosomal-recessive dilated cardiomyopathy

Coenzyme A (CoA) is an essential cofactor involved in a range of metabolic pathways including the activation of long-chain fatty acids for catabolism. Cells synthesize CoA de novo from vitamin B5 (pantothenate) via a pathway strongly conserved across prokaryotes and eukaryotes. In humans, it involves five enzymatic steps catalyzed by four enzymes: pantothenate kinase (PANK [isoforms 1-4]), 4'-phosphopantothenoylcysteine synthetase (PPCS), phosphopantothenoylcysteine decarboxylase (PPCDC), and CoA synthase (COASY). To date, inborn errors of metabolism associated with all of these genes, except PPCDC, have been described, two related to neurodegeneration with brain iron accumulation (NBIA), and one associated with a cardiac phenotype. This paper reports another defect in this pathway (detected in two sisters), associated with a fatal cardiac phenotype, caused by biallelic variants (p.Thr53Pro and p.Ala95Val) of PPCDC. PPCDC enzyme (EC 4.1.1.36) catalyzes the decarboxylation of 4'-phosphopantothenoylcysteine to 4'-phosphopantetheine in CoA biosynthesis. The variants p.Thr53Pro and p.Ala95Val affect residues highly conserved across different species; p.Thr53Pro is involved in the binding of flavin mononucleotide, and p.Ala95Val is likely a destabilizing mutation. Patient-derived fibroblasts showed an absence of PPCDC protein, and nearly 50% reductions in CoA levels. The cells showed clear energy deficiency problems, with defects in mitochondrial respiration, and mostly glycolytic ATP synthesis. Functional studies performed in yeast suggest these mutations to be functionally relevant. In summary, this work describes a new, ultra-rare, severe inborn error of metabolism due to pathogenic variants of PPCDC.

Sharing resources to advance translational research

The publication of Resource articles is essential for the dissemination of novel, or substantially enhanced, tools, techniques, disease models, datasets and resources. By sharing knowledge and resources in a globally accessible manner, we can support human disease research to accelerate the translation of fundamental discoveries to effective treatments or diagnostics for diverse patient populations. To promote and encourage excellence in Resource articles, Disease Models & Mechanisms (DMM) is launching a new 'Outstanding Resource Paper Prize'. To celebrate this, we highlight recent outstanding DMM Resource articles that have the ultimate goal of benefitting of human health.

Genetic variance in human disease - modelling the future of genomic medicine

Summary: DMM is launching a new Subject Focus on genetic variance in human disease. Here, we discuss this ongoing series of invited articles and reflect on advances in understanding the genotype–phenotype complexities in disease.