Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

<p>Background:</p> <p>The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network.</p> <p>Results:</p> <p>Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions.</p> <p>Conclusion:</p> <p>We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions.</p

Constraint-based modeling of yeast metabolism and protein secretion

Yeasts are extensively exploited as cell factories for producing alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. To improve the performance of yeast cell factories, it is necessary to understand their metabolism. Genome-scale metabolic models (GEMs) have been widely used to study cellular metabolism systematically. However, GEMs for yeast species have not been equally developed. GEMs for the well-studied yeasts such as Saccharomyces cerevisiae have been updated several times, while most of the other yeast species have no available GEM. Additionally, classical GEMs only account for the metabolic reactions, which limits their usage to study complex phenotypes that are not controlled by metabolism alone. Thus, other biological processes can be integrated with GEMs to fulfill diverse research purposes. \ua0In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production. \ua0When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale

The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

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, ilN800 that includes a more rigorous and detailed descrition 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 ilN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of ilN800, 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. Conclusions: Performing integrated analyses using ilN800 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

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public

Connecting extracellular metabolomic measurements to intracellular flux states in yeast

<p>Abstract</p> <p>Background</p> <p>Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.</p> <p>Results</p> <p>We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, <it>i</it>MM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The <it>i</it>MM904 metabolic network was reconstructed based on an existing genome-scale network, <it>i</it>ND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the <it>i</it>MM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed.</p> <p>Conclusion</p> <p>Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states.</p

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