A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics.
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ABSTRACT:
SUBMITTER: Oftadeh O
PROVIDER: S-EPMC8352978 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. RESULTS: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. CONCLUSION: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.
| S-EPMC2542360 | biostudies-literature
Project description:Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference, when calculated with flux balance analysis, has not been studied in detail before. Here, the wild-type simulations of selected GEMs for Saccharomyces cerevisiae have been analysed and most of the models tested predicted erroneous fluxes in central pathways, especially in the pentose phosphate pathway. Since the problematic fluxes were mostly related to areas of the metabolism consuming or producing NADPH/NADH, we have manually curated all reactions including these cofactors by forcing the use of NADPH/NADP+ in anabolic reactions and NADH/NAD+ for catabolic reactions. The curated models predicted more accurate flux distributions and performed better in the simulation of mutant phenotypes.
| S-EPMC5779720 | biostudies-literature
Project description:The function of a protein generally is determined by its three-dimensional (3D) structure. Thus, it would be useful to know the 3D structure of the thousands of protein sequences that are emerging from the many genome projects. To this end, fold assignment, comparative protein structure modeling, and model evaluation were automated completely. As an illustration, the method was applied to the proteins in the Saccharomyces cerevisiae (baker's yeast) genome. It resulted in all-atom 3D models for substantial segments of 1,071 (17%) of the yeast proteins, only 40 of which have had their 3D structure determined experimentally. Of the 1,071 modeled yeast proteins, 236 were related clearly to a protein of known structure for the first time; 41 of these previously have not been characterized at all.
| S-EPMC24864 | biostudies-literature
Project description:Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.
| S-EPMC5572397 | biostudies-literature
Project description:Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference in nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1,229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine-learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.
| S-SCDT-10_1038-S44320-024-00060-7 | biostudies-other
Project description:Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for approximately 70-80% of the conditions considered.
| S-EPMC263729 | biostudies-literature
Project description:Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.
| S-EPMC4598104 | biostudies-literature
Project description:BackgroundGenome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.ResultsBased on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model's phenotype prediction ability, and extended the model's potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale [Formula: see text] values, predicting the differential expression of enzymes under different growth conditions.ConclusionsThis study shows that incorporating enzymes' abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.
| S-EPMC8247156 | biostudies-literature
Project description:Saccharomyces kudriavzevii is a cold-tolerant species identified as a good alternative for industrial winemaking. Although S. kudriavzevii has never been found in winemaking, its co-occurrence with Saccharomyces cerevisiae in Mediterranean oaks is well documented. This sympatric association is believed to be possible due to the different growth temperatures of the two yeast species. However, the mechanisms behind the cold tolerance of S. kudriavzevii are not well understood. In this work, we propose the use of a dynamic genome-scale model to compare the metabolic routes used by S. kudriavzevii at two temperatures, 25°C and 12°C, to decipher pathways relevant to cold tolerance. The model successfully recovered the dynamics of biomass and external metabolites and allowed us to link the observed phenotype with exact intracellular pathways. The model predicted fluxes that are consistent with previous findings, but it also led to novel results which we further confirmed with intracellular metabolomics and transcriptomic data. The proposed model (along with the corresponding code) provides a comprehensive picture of the mechanisms of cold tolerance that occur within S. kudriavzevii. The proposed strategy offers a systematic approach to explore microbial diversity from extracellular fermentation data at low temperatures. IMPORTANCE Nonconventional yeasts promise to provide new metabolic pathways for producing industrially relevant compounds and tolerating specific stressors such as cold temperatures. The mechanisms behind the cold tolerance of S. kudriavzevii or its sympatric relationship with S. cerevisiae in Mediterranean oaks are not well understood. This study proposes a dynamic genome-scale model to investigate metabolic pathways relevant to cold tolerance. The predictions of the model would indicate the ability of S. kudriavzevii to produce assimilable nitrogen sources from extracellular proteins present in its natural niche. These predictions were further confirmed with metabolomics and transcriptomic data. This finding suggests that not only the different growth temperature preferences but also this proteolytic activity may contribute to the sympatric association with S. cerevisiae. Further exploration of these natural adaptations could lead to novel engineering targets for the biotechnological industry.
| S-EPMC10269563 | biostudies-literature
Project description:Former studies have established that loss of heterozygosity can be a key driver of sequence evolution in unicellular eukaryotes and tissues of metazoans. However, little is known about whether the distribution of loss of heterozygosity events is largely random or forms discernible patterns across genomes. To initiate our experiments, we introduced selectable markers to both arms of all chromosomes of the budding yeast. Subsequent extensive assays, repeated over several genetic backgrounds and environments, provided a wealth of information on the genetic and environmental determinants of loss of heterozygosity. Three findings stand out. First, the number of loss of heterozygosity events per unit time was more than 25 times higher for growing than starving cells. Second, loss of heterozygosity was most frequent when regions of homology around a recombination site were identical, about a half-% sequence divergence was sufficient to reduce its incidence. Finally, the density of loss of heterozygosity events was highly dependent on the genome's physical architecture. It was several-fold higher on short chromosomal arms than on long ones. Comparably large differences were seen within a single arm where regions close to a centromere were visibly less affected than regions close, though usually not strictly adjacent, to a telomere. We suggest that the observed uneven distribution of loss of heterozygosity events could have been caused not only by an uneven density of initial DNA damages. Location-depended differences in the mode of DNA repair, or its effect on fitness, were likely to operate as well.
| S-EPMC9071580 | biostudies-literature