1
|
Nguyen V, Li Y, Lu T. Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae. ACS Synth Biol 2024. [PMID: 38657170 DOI: 10.1021/acssynbio.3c00542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.
Collapse
Affiliation(s)
- Viviana Nguyen
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
| | - Yifei Li
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| |
Collapse
|
2
|
van Sluijs B, Zhou T, Helwig B, Baltussen MG, Nelissen FHT, Heus HA, Huck WTS. Iterative design of training data to control intricate enzymatic reaction networks. Nat Commun 2024; 15:1602. [PMID: 38383500 PMCID: PMC10881569 DOI: 10.1038/s41467-024-45886-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.
Collapse
Affiliation(s)
- Bob van Sluijs
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Tao Zhou
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands.
| | - Britta Helwig
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Mathieu G Baltussen
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Frank H T Nelissen
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Hans A Heus
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Wilhelm T S Huck
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands.
| |
Collapse
|
3
|
Ridder MD, van den Brandeler W, Altiner M, Daran-Lapujade P, Pabst M. Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast. Mol Cell Proteomics 2023; 22:100552. [PMID: 37076048 DOI: 10.1016/j.mcpro.2023.100552] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/23/2023] [Accepted: 04/13/2023] [Indexed: 04/21/2023] Open
Abstract
The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.
Collapse
Affiliation(s)
- Maxime den Ridder
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Wiebeke van den Brandeler
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Meryem Altiner
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Pascale Daran-Lapujade
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands.
| | - Martin Pabst
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands.
| |
Collapse
|
4
|
Lao-Martil D, Schmitz JPJ, Teusink B, van Riel NAW. Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling. Metab Eng 2023; 77:128-142. [PMID: 36963461 DOI: 10.1016/j.ymben.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/12/2023] [Accepted: 03/05/2023] [Indexed: 03/26/2023]
Abstract
Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.
Collapse
Affiliation(s)
- David Lao-Martil
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Noord-Brabant, 5612AE, the Netherlands
| | - Joep P J Schmitz
- DSM Biotechnology Center, Delft, Zuid-Holland, 2613AX, the Netherlands
| | - Bas Teusink
- Systems Biology Lab, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, 1081HZ, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Noord-Brabant, 5612AE, the Netherlands; Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Noord-Holland, 1105AZ, the Netherlands.
| |
Collapse
|
5
|
Ting TY, Li Y, Bunawan H, Ramzi AB, Goh HH. Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae. J Biosci Bioeng 2023; 135:259-265. [PMID: 36803862 DOI: 10.1016/j.jbiosc.2023.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/03/2023] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.
Collapse
Affiliation(s)
- Tiew-Yik Ting
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - YaDong Li
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Hamidun Bunawan
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Ahmad Bazli Ramzi
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Hoe-Han Goh
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| |
Collapse
|
6
|
Minden S, Aniolek M, Sarkizi Shams Hajian C, Teleki A, Zerrer T, Delvigne F, van Gulik W, Deshmukh A, Noorman H, Takors R. Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli. Metabolites 2022; 12:metabo12030263. [PMID: 35323706 PMCID: PMC8953226 DOI: 10.3390/metabo12030263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.
Collapse
Affiliation(s)
- Steven Minden
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Maria Aniolek
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Christopher Sarkizi Shams Hajian
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Attila Teleki
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Tobias Zerrer
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Frank Delvigne
- Microbial Processes and Interactions (MiPI), TERRA Research and Teaching Centre, Gembloux Agro Bio Tech, University of Liege, 5030 Gembloux, Belgium;
| | - Walter van Gulik
- Department of Biotechnology, Delft University of Technology, van der Maasweg 6, 2629 HZ Delft, The Netherlands;
| | - Amit Deshmukh
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
| | - Henk Noorman
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
- Department of Biotechnology, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
- Correspondence:
| |
Collapse
|