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Kleijn IT, Marguerat S, Shahrezaei V. A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes. J R Soc Interface 2023; 20:20230206. [PMID: 37751876 PMCID: PMC10522411 DOI: 10.1098/rsif.2023.0206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws, but current models do not account for the uptake of compound sources of carbon and nitrogen. Here, we formulate a C-GRAM with nitrogen and carbon pathways converging on biomass production, with parametrizations accounting for respirofermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximized the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state.
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Affiliation(s)
- Istvan T. Kleijn
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
- Ralph Lauren Centre for Breast Cancer Research, The Royal Marsden NHS Foundation Trust, London, UK
| | - Samuel Marguerat
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK
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2
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Weith M, Großbach J, Clement‐Ziza M, Gillet L, Rodríguez‐López M, Marguerat S, Workman CT, Picotti P, Bähler J, Aebersold R, Beyer A. Genetic effects on molecular network states explain complex traits. Mol Syst Biol 2023; 19:e11493. [PMID: 37485750 PMCID: PMC10407735 DOI: 10.15252/msb.202211493] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.
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Affiliation(s)
- Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Jan Großbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | | | - Ludovic Gillet
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - María Rodríguez‐López
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Samuel Marguerat
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Christopher T Workman
- Department of Biotechnology and BiomedicineTechnical University of DenmarkLyngbyDenmark
| | - Paola Picotti
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Jürg Bähler
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
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Bertaux F, Kleijn IT, Marguerat S, Shahrezaei V. Fission yeast obeys a linear size law under nutrient titration. MICROPUBLICATION BIOLOGY 2023; 2023:10.17912/micropub.biology.000833. [PMID: 37193545 PMCID: PMC10182418 DOI: 10.17912/micropub.biology.000833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 04/27/2023] [Accepted: 04/24/2023] [Indexed: 05/18/2023]
Abstract
Steady-state cell size and geometry depend on growth conditions. Here, we use an experimental setup based on continuous culture and single-cell imaging to study how cell volume, length, width and surface-to-volume ratio vary across a range of growth conditions including nitrogen and carbon titration, the choice of nitrogen source, and translation inhibition. Overall, we find cell geometry is not fully determined by growth rate and depends on the specific mode of growth rate modulation. However, under nitrogen and carbon titrations, we observe that the cell volume and the growth rate follow the same linear scaling.
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Affiliation(s)
- François Bertaux
- MRC London Institute of Medical Sciences, London, England, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, England, United Kingdom
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, England, United Kingdom
| | - Istvan T. Kleijn
- MRC London Institute of Medical Sciences, London, England, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, England, United Kingdom
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, England, United Kingdom
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences, London, England, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, England, United Kingdom
- Current address: UCL Cancer Institute, University College London, London, England, United Kingdom
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, England, United Kingdom
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Messner CB, Demichev V, Muenzner J, Aulakh SK, Barthel N, Röhl A, Herrera-Domínguez L, Egger AS, Kamrad S, Hou J, Tan G, Lemke O, Calvani E, Szyrwiel L, Mülleder M, Lilley KS, Boone C, Kustatscher G, Ralser M. The proteomic landscape of genome-wide genetic perturbations. Cell 2023; 186:2018-2034.e21. [PMID: 37080200 PMCID: PMC7615649 DOI: 10.1016/j.cell.2023.03.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/20/2023] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.
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Affiliation(s)
- Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265 Davos, Switzerland
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Julia Muenzner
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Simran K Aulakh
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Natalie Barthel
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Annika Röhl
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | | | - Anna-Sophia Egger
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Jing Hou
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Oliver Lemke
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Enrica Calvani
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Lukasz Szyrwiel
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Michael Mülleder
- Charité Universitätsmedizin, Core Facility - High Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S3E1, Canada; The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; RIKEN Center for Sustainable Resource Science, Wako, 351-0198 Saitama, Japan
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, Scotland, UK.
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
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A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast. mSystems 2022; 7:e0042322. [PMID: 35950759 PMCID: PMC9426579 DOI: 10.1128/msystems.00423-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models.
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