| 1. |
- Lu, Hongzhong, 1987, et al.
(författare)
-
A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
- 2019
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
|
|
| 2. |
- Ishchuk, Olena, 1980, et al.
(författare)
-
Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae
- 2022
-
Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:30
-
Tidskriftsartikel (refereegranskat)abstract
- Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3- δshm1-HEM2-δhmx1-FET4-δgcv2-HEM1-δgcv1-HEM13), which produces 70-foldhigher levels of intracellular heme.
|
|
| 3. |
|
|
| 4. |
- Wang, Hao, 1975, et al.
(författare)
-
RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor
- 2018
-
Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 14:10, s. e1006541-
-
Tidskriftsartikel (refereegranskat)abstract
- RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).
|
|
| 5. |
- Domenzain Del Castillo Cerecer, Iván, 1991, et al.
(författare)
-
Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
- 2022
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
|
|
| 6. |
- Ferreira, Raphael, 1990, et al.
(författare)
-
Model-Assisted Fine-Tuning of Central Carbon Metabolism in Yeast through dCas9-Based Regulation
- 2019
-
Ingår i: ACS Synthetic Biology. - : American Chemical Society (ACS). - 2161-5063. ; 8:11, s. 2457-2463
-
Tidskriftsartikel (refereegranskat)abstract
- Engineering Saccharomyces cerevisiae for industrial-scale production of valuable chemicals involves extensive modulation of its metabolism. Here, we identified novel gene expression fine-tuning set-ups to enhance endogenous metabolic fluxes toward increasing levels of acetyl-CoA and malonyl-CoA. dCas9-based transcriptional regulation was combined together with a malonyl-CoA responsive intracellular biosensor to select for beneficial set-ups. The candidate genes for screening were predicted using a genome-scale metabolic model, and a gRNA library targeting a total of 168 selected genes was designed. After multiple rounds of fluorescence-activated cell sorting and library sequencing, the gRNAs that were functional and increased flux toward malonyl-CoA were assessed for their efficiency to enhance 3-hydroxypropionic acid (3-HP) production. 3-HP production was significantly improved upon fine-tuning genes involved in providing malonyl-CoA precursors, cofactor supply, as well as chromatin remodeling.
|
|
| 7. |
- Lahtvee, Petri-Jaan, 1985, et al.
(författare)
-
Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast
- 2017
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 4:5, s. 495-504.e5
-
Tidskriftsartikel (refereegranskat)abstract
- Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.
|
|
| 8. |
- Sánchez, Benjamín José, 1988, et al.
(författare)
-
Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae
- 2021
-
Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 21:6
-
Tidskriftsartikel (refereegranskat)abstract
- Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.
|
|
| 9. |
- Sánchez, Benjamín José, 1988
(författare)
-
Computing abundance constraints in Saccharomyces cerevisiae’s metabolism
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The unicellular eukaryotic organism Saccharomyces cerevisiae (budding yeast) is routinely used for production of high-value chemical compounds in the biotechnology industry. To improve production yields, it is fundamental to understand cellular metabolism, i.e. all biochemical reactions that occur inside the cell. In the past 20 years, genome-scale metabolic models (GEMs) have risen as computational tools for simulating all possible metabolic phenotypes that the cell can attain, while respecting constraints such as mass balances and reaction reversibilities. However, the number of metabolic states bound to only those constraints is infinite; therefore, it becomes necessary to include additional condition-specific constraints. Moreover, we would like these constraints to reflect physical limitations inside the cell, avoiding arbitrary ad-hoc bounds. In this thesis, approaches for including abundance constraints (i.e. constraints based on absolute abundances of different biomolecules) are evaluated in a GEM of S. cerevisiae . First, the GEM approach and how it has been used in S. cerevisiae is reviewed, identifying key areas for development. Afterwards, the concepts of sustainable model development and multi-layer experimental data generation are presented as foundation stones for constructing integrative analysis. Regarding the first concept, a systematic way of recording changes in a GEM using a version-controlled system is introduced, allowing reproducibility and open collaboration from the community. Regarding the second concept, a multi-omics dataset of yeast grown under different temperature, osmotic and ethanol stresses is presented and used throughout the thesis for studying metabolism. The major part of this work focuses on the integration into GEMs of abundance data of two types of bio-molecules: lipids and enzymes. First, a method for integrating lipid requirements in an unbiased way (SLIMEr) is presented and implemented for yeast, to show that lipid metabolism can be re-arranged without spending high amounts of energy. Secondly, a method for adding so-called “enzyme constraints” into a GEM (GECKO) is developed. These enzyme constraints limit reaction rates by the absolute abundance of enzymes, and prove to be crucial for explaining yeast physiology and computing enzyme usage in metabolism. Thirdly, the quantification technique used for estimating enzyme abundances is analyzed in terms of accuracy and precision, and further improved by varying the normalization and scaling steps. Finally, GECKO is used on the stress dataset to create enzyme-constrained models of yeast representing each stress condition. This allows comparing the distribution of enzyme usage within and between conditions, highlighting enzymes that play an important role in the metabolic response to stress.
|
|
| 10. |
- Sánchez, Benjamín José, 1988, et al.
(författare)
-
SLIMEr: Probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework
- 2019
-
Ingår i: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Background: A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. Results: SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can represent accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. Conclusions: The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr.
|
|
| 11. |
- Sanchez Barja, Benjamin José, 1988, et al.
(författare)
-
Genome scale models of yeast: towards standardized evaluation and consistent omic integration
- 2015
-
Ingår i: Integrative Biology (United Kingdom). - 1757-9694 .- 1757-9708. ; 7:8, s. 846-858
-
Tidskriftsartikel (refereegranskat)abstract
- Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.
|
|
| 12. |
- Sanchez Barja, Benjamin José, 1988, et al.
(författare)
-
Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
- 2017
-
Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8, s. Article no 935 -
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 13. |
- Xia, Jianye, 1980, et al.
(författare)
-
Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation
- 2022
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Saccharomyces cerevisiae is a widely used cell factory; therefore, it is important to understand how it organizes key functional parts when cultured under different conditions. Here, we perform a multiomics analysis of S. cerevisiae by culturing the strain with a wide range of specific growth rates using glucose as the sole limiting nutrient. Under these different conditions, we measure the absolute transcriptome, the absolute proteome, the phosphoproteome, and the metabolome. Most functional protein groups show a linear dependence on the specific growth rate. Proteins engaged in translation show a perfect linear increase with the specific growth rate, while glycolysis and chaperone proteins show a linear decrease under respiratory conditions. Glycolytic enzymes and chaperones, however, show decreased phosphorylation with increasing specific growth rates; at the same time, an overall increased flux through these pathways is observed. Further analysis show that even though mRNA levels do not correlate with protein levels for all individual genes, the transcriptome level of functional groups correlates very well with its corresponding proteome. Finally, using enzyme-constrained genome-scale modeling, we find that enzyme usage plays an important role in controlling flux in amino acid biosynthesis. Understanding how yeast organizes its functional proteome is a fundamental task in systems biology. Here, the authors conduct a multiomics analysis on yeast cells cultured with different growth rates, identifying a linear dependence of the functional proteome on the growth rate.
|
|
| 14. |
- Zhang, Chengyu, et al.
(författare)
-
Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
- 2024
-
Ingår i: Molecular Systems Biology. - 1744-4292. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- 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 for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 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.
|
|
| 15. |
- Zhang, J., et al.
(författare)
-
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
- 2020
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
|
|
