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A comprehensive mechanistic model of iron metabolism in Saccharomyces cerevisiae.

ABSTRACT: 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.

SUBMITTER: Lindahl PA

PROVIDER: S-EPMC7179950 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A comprehensive mechanistic model of iron metabolism in Saccharomyces cerevisiae.

Lindahl Paul A PA

Metallomics : integrated biometal science 20190918 11

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 mu ...[more]

PMID: 31531508

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Project description:BackgroundIron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism.ResultsUsing functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism.ConclusionComparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.

Project description:BackgroundConsiderable interest in the bioconversion of lignocellulosic biomass into ethanol has led to metabolic engineering of Saccharomyces cerevisiae for fermentation of xylose. In the present study, the transcriptome and proteome of recombinant, xylose-utilising S. cerevisiae grown in aerobic batch cultures on xylose were compared with those of glucose-grown cells both in glucose repressed and derepressed states. The aim was to study at the genome-wide level how signalling and carbon catabolite repression differ in cells grown on either glucose or xylose. The more detailed knowledge whether xylose is sensed as a fermentable carbon source, capable of catabolite repression like glucose, or is rather recognised as a non-fermentable carbon source is important for further engineering this yeast for more efficient anaerobic fermentation of xylose.ResultsGenes encoding respiratory proteins, proteins of the tricarboxylic acid and glyoxylate cycles, and gluconeogenesis were only partially repressed by xylose, similar to the genes encoding their transcriptional regulators HAP4, CAT8 and SIP1-2 and 4. Several genes that are repressed via the Snf1p/Mig1p-pathway during growth on glucose had higher expression in the cells grown on xylose than in the glucose repressed cells but lower than in the glucose derepressed cells. The observed expression profiles of the transcription repressor RGT1 and its target genes HXT2-3, encoding hexose transporters suggested that extracellular xylose was sensed by the glucose sensors Rgt2p and Snf3p. Proteome analyses revealed distinct patterns in phosphorylation of hexokinase 2, glucokinase and enolase isoenzymes in the xylose- and glucose-grown cells.ConclusionThe results indicate that the metabolism of yeast growing on xylose corresponds neither to that of fully glucose repressed cells nor that of derepressed cells. This may be one of the major reasons for the suboptimal fermentation of xylose by recombinant S. cerevisiae strains. Phosphorylation of different isoforms of glycolytic enzymes suggests that regulation of glycolysis also occurred at a post-translational level, supporting prior findings.

Project description:Kinetic parameters describing hepatic uptake in hepatocytes are frequently estimated without appropriate incorporation of bidirectional passive diffusion, intracellular binding, and metabolism. A mechanistic two-compartment model was developed to describe all of the processes occurring during the in vitro uptake experiments performed in freshly isolated rat hepatocytes plated for 2 h. Uptake of rosuvastatin, pravastatin, pitavastatin, valsartan, bosentan, telmisartan, and repaglinide was investigated over a 0.1 to 300 ?M concentration range at 37°C for 2 or 45-90 min; nonspecific binding was taken into account. All concentration-time points were analyzed simultaneously by using a mechanistic two-compartment model describing uptake kinetics [unbound affinity constant (K(m,u)), maximum uptake rate (V(max)), unbound active uptake clearance (CL(active,u))], passive diffusion [unbound passive diffusion clearance (P(diff,u))], and intracellular binding [intracellular unbound fraction (fu(cell))]. When required (telmisartan and repaglinide), the model was extended to account for the metabolism [unbound metabolic clearance (CL(met,u))]. The CL(active,u) ranged 8-fold, reflecting a 11-fold range in uptake K(m,u), with telmisartan and valsartan showing the highest affinity for uptake transporters (K(m,u) <10 ?M). Both P(diff,u) and fu(cell) span over two orders of magnitude and reflected the lipophilicity of the drugs in the dataset. An extended incubation time allowed steady state to be reached between media and intracellular compartment concentrations and reduced the error in certain parameter estimates observed with shorter incubation times. Active transport accounted for >70% of total uptake for all drugs investigated and was 4- and 112-fold greater than CL(met,u) for telmisartan and repaglinide, respectively. Modeling of uptake kinetics in conjunction with metabolism improved the precision of the uptake parameter estimates for repaglinide and telmisartan. Recommendations are made for uptake experimental design and modeling strategies.

Dataset Information

A comprehensive mechanistic model of iron metabolism in Saccharomyces cerevisiae.

Publications

A comprehensive mechanistic model of iron metabolism in Saccharomyces cerevisiae.

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