Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Involvement of Bacterial and Fungal Extracellular Products in Transformation of Manganese-Bearing Minerals and Its Environmental Impact

Manganese oxides are considered an essential component of natural geochemical barriers due to their redox and sorptive reactivity towards essential and potentially toxic trace elements. Despite the perception that they are in a relatively stable phase, microorganisms can actively alter the prevailing conditions in their microenvironment and initiate the dissolution of minerals, a process that is governed by various direct (enzymatic) or indirect mechanisms. Microorganisms are also capable of precipitating the bioavailable manganese ions via redox transformations into biogenic minerals, including manganese oxides (e.g., low-crystalline birnessite) or oxalates. Microbially mediated transformation influences the (bio)geochemistry of manganese and also the environmental chemistry of elements intimately associated with its oxides. Therefore, the biodeterioration of manganese-bearing phases and the subsequent biologically induced precipitation of new biogenic minerals may inevitably and severely impact the environment. This review highlights and discusses the role of microbially induced or catalyzed processes that affect the transformation of manganese oxides in the environment as relevant to the function of geochemical barriers.

Metal ion availability and homeostasis as drivers of metabolic evolution and enzyme function

Metal ions are potent catalysts and have been available for cellular biochemistry at all stages of evolution. Growing evidence suggests that metal catalysis was critical for the origin of the very first metabolic reactions. With approximately 80% of modern metabolic pathways being dependent on metal ions, metallocatalysis and homeostasis continue to be essential for intracellular metabolic networks and physiology. However, the genetic network that controls metal ion homeostasis and the impact of metal availability on metabolism is poorly understood. Here, we review recent work on gene and protein evolution relevant for better understanding metal ion biology and its role in metabolism. We highlight the importance of analysing the origin and evolution of enzyme catalysis in the context of catalytically relevant metal ions, summarise unanswered questions essential for developing a comprehensive understanding of metal ion homeostasis and advocate for the consideration of metal ion properties and availability in the design and directed evolution of novel enzymes and pathways.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

Metal ions are essential to all living cells, as they can serve as cofactors of enzymes to drive catalysis of biochemical reactions. We present a constraint-based model of yeast that relates metabolism with metal ions via enzymes. The model is able to capture responses of metabolism and gene expression upon iron depletion, suggesting that yeast cells allocate iron resource in the way abiding to optimization principles. Interestingly, the model predicts up-regulation of several iron-containing enzymes that coincide with experiments, which raises the possibility that the decrease in activity due to limited iron could be compensated by elevated enzyme abundance. Moreover, the model paves the way for guiding biosynthesis of high-value compounds (e.g., p-coumaric acid) that relies on iron-containing enzymes.

Metal ions are vital to metabolism, as they can act as cofactors on enzymes and thus modulate individual enzymatic reactions. Although many enzymes have been reported to interact with metal ions, the quantitative relationships between metal ions and metabolism are lacking. Here, we reconstructed a genome-scale metabolic model of the yeast Saccharomyces cerevisiae to account for proteome constraints and enzyme cofactors such as metal ions, named CofactorYeast. The model is able to estimate abundances of metal ions binding on enzymes in cells under various conditions, which are comparable to measured metal ion contents in biomass. In addition, the model predicts distinct metabolic flux distributions in response to reduced levels of various metal ions in the medium. Specifically, the model reproduces changes upon iron deficiency in metabolic and gene expression levels, which could be interpreted by optimization principles (i.e., yeast optimizes iron utilization based on metabolic network and enzyme kinetics rather than preferentially targeting iron to specific enzymes or pathways). At last, we show the potential of using the model for understanding cell factories that harbor heterologous iron-containing enzymes to synthesize high-value compounds such as p-coumaric acid. Overall, the model demonstrates the dependence of enzymes on metal ions and links metal ions to metabolism on a genome scale.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

Iron Regulatory Mechanisms in Saccharomyces cerevisiae

Iron is an essential micronutrient for all eukaryotic organisms because it participates as a redox cofactor in many cellular processes. However, excess iron can damage cells since it promotes the generation of reactive oxygen species. The budding yeast Saccharomyces cerevisiae has been used as a model organism to study the adaptation of eukaryotic cells to changes in iron availability. Upon iron deficiency, yeast utilizes two transcription factors, Aft1 and Aft2, to activate the expression of a set of genes known as the iron regulon, which are implicated in iron uptake, recycling and mobilization. Moreover, Aft1 and Aft2 activate the expression of Cth2, an mRNA-binding protein that limits the expression of genes encoding for iron-containing proteins or that participate in iron-using processes. Cth2 contributes to prioritize iron utilization in particular pathways over other highly iron-consuming and non-essential processes including mitochondrial respiration. Recent studies have revealed that iron deficiency also alters many other metabolic routes including amino acid and lipid synthesis, the mitochondrial retrograde response, transcription, translation and deoxyribonucleotide synthesis; and activates the DNA damage and general stress responses. At high iron levels, the yeast Yap5, Msn2, and Msn4 transcription factors activate the expression of a vacuolar iron importer called Ccc1, which is the most important high-iron protecting factor devoted to detoxify excess cytosolic iron that is stored into the vacuole for its mobilization upon scarcity. The complete sequencing and annotation of many yeast genomes is starting to unveil the diversity and evolution of the iron homeostasis network in this species.

Human Serum Albumin Facilitates Heme-Iron Utilization by Fungi

Heme constitutes a major iron source for microorganisms and particularly for pathogenic microbes; to overcome the iron scarcity in the animal host, many pathogenic bacteria and fungi have developed systems to extract and take up heme from host proteins such as hemoglobin. Microbial heme uptake mechanisms are usually studied using growth media containing free heme or hemoglobin as a sole iron source. However, the animal host contains heme-scavenging proteins that could prevent this uptake. In the human host in particular, the most abundant serum heme-binding protein is albumin. Surprisingly, however, we found that in the case of fungi of the Candida species family, albumin promoted rather than prevented heme utilization. Albumin thus constitutes a human-specific factor that can affect heme-iron utilization and could serve as target for preventing heme-iron utilization by fungal pathogens. As a proof of principle, we identify two drugs that can inhibit albumin-stimulated heme utilization.

A large portion of biological iron is found in the form of an iron-protoporphyrin IX complex, or heme. In the human host environment, which is exceptionally poor in free iron, heme iron, particularly from hemoglobin, constitutes a major source of iron for invading microbial pathogens. Several fungi were shown to utilize free heme, and Candida albicans, a major opportunistic pathogen, is able both to capture free heme and to extract heme from hemoglobin using a network of extracellular hemophores. Human serum albumin (HSA) is the most abundant host heme-scavenging protein. Tight binding of heme by HSA restricts its toxic chemical reactivity and could diminish its availability as an iron source for pathogenic microbes. We found, however, that rather than inhibiting heme utilization, HSA greatly increases availability of heme as an iron source for C. albicans and other fungi. In contrast, hemopexin, a low-abundance but high-affinity heme-scavenging serum protein, does inhibit heme utilization by C. albicans. However, inhibition by hemopexin is mitigated in the presence of HSA. Utilization of albumin-bound heme requires the same hemophore cascade as that which mediates hemoglobin-iron utilization. Accordingly, we found that the C. albicans hemophores are able to extract heme bound to HSA in vitro. Since many common drugs are known to bind to HSA, we tested whether they could interfere with heme-iron utilization. We show that utilization of albumin-bound heme by C. albicans can be inhibited by the anti-inflammatory drugs naproxen and salicylic acid.

Metabolic response to Parkinson's disease recapitulated by the haploinsufficient diploid yeast cells hemizygous for the adrenodoxin reductase gene

Adrenodoxin reductase, a widely conserved mitochondrial P450 protein, catalyses essential steps in steroid hormone biosynthesis and is highly expressed in the adrenal cortex. The yeast adrenodoxin reductase homolog, Arh1p, is involved in cytoplasmic and mitochondrial iron homeostasis and is required for activity of enzymes containing an Fe-S cluster. In this paper, we investigated the response of yeast to the loss of a single copy of ARH1, an oxidoreductase of the mitochondrial inner membrane, which is among the few mitochondrial proteins that is essential for viability in yeast. The phenotypic, transcriptional, proteomic, and metabolic landscape indicated that Saccharomyces cerevisiae successfully adapted to this loss, displaying an apparently dosage-insensitive cellular response. However, a considered investigation of transcriptional regulation in ARH1-impaired yeast highlighted that a significant hierarchical reorganisation occurred, involving the iron assimilation and tyrosine biosynthetic processes. The interconnected roles of the iron and tyrosine pathways, coupled with oxidative processes, are of interest beyond yeast since they are involved in dopaminergic neurodegeneration associated with Parkinson's disease. The identification of similar responses in yeast, albeit preliminary, suggests that this simple eukaryote could have potential as a model system for investigating the regulatory mechanisms leading to the initiation and progression of early disease responses in humans.

A comprehensive mechanistic model of iron metabolism in Saccharomyces cerevisiae

The ironome of budding yeast (circa 2019) consists of approximately 139 proteins and 5 nonproteinaceous species. These proteins were grouped according to location in the cell, type of iron center(s), and cellular function. The resulting 27 groups were used, along with an additional 13 nonprotein components, to develop a mesoscale mechanistic model that describes the import, trafficking, metallation, and regulation of iron within growing yeast cells. The model was designed to be simultaneously mutually autocatalytic and mutually autoinhibitory - a property called autocatinhibitory that should be most realistic for simulating cellular biochemical processes. The model was assessed at the systems' level. General conclusions are presented, including a new perspective on understanding regulatory mechanisms in cellular systems. Some unsettled issues are described. This model, once fully developed, has the potential to mimic the phenotype (at a coarse-grain level) of all iron-related genetic mutations in this simple and well-studied eukaryote.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor‐utilising enzymes and transporters through constraint‐based modelling. The first eukaryotic genome‐scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron‐sulphur (Fe‐S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron‐recruiting enzyme at a rate of 3.02 × 10 ⁻¹¹ mmol·(g biomass) ⁻¹·h  ⁻¹.