1
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Stukenberg D, Faber A, Becker A. Graded-CRISPRi, a Tool for Tuning the Strengths of CRISPRi-Mediated Knockdowns in Vibrio natriegens Using gRNA Libraries. ACS Synth Biol 2024; 13:2091-2104. [PMID: 38916455 DOI: 10.1021/acssynbio.4c00056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
In recent years, the fast-growing bacterium Vibrio natriegens has gained increasing attention as it has the potential to become a next-generation chassis for synthetic biology. A wide range of genetic parts and genome engineering methods have already been developed. However, there is still a need for a well-characterized tool to effectively and gradually reduce the expression levels of native genes. To bridge this gap, we created graded-CRISPRi, a system utilizing gRNA variants that lead to varying levels of repression strength. By incorporating multiple gRNA sequences into our design, we successfully extended this concept to simultaneously repress four distinct reporter genes. Furthermore, we demonstrated the capability of using graded-CRISPRi to target native genes, thereby examining the effect of various knockdown levels on growth.
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Affiliation(s)
- Daniel Stukenberg
- Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg 35037, Germany
- Department of Biology, Philipps-Universität Marburg, Marburg 35037, Germany
| | - Anna Faber
- Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg 35037, Germany
- Department of Biology, Philipps-Universität Marburg, Marburg 35037, Germany
| | - Anke Becker
- Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg 35037, Germany
- Department of Biology, Philipps-Universität Marburg, Marburg 35037, Germany
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2
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Holbrook-Smith D, Trouillon J, Sauer U. Metabolomics and Microbial Metabolism: Toward a Systematic Understanding. Annu Rev Biophys 2024; 53:41-64. [PMID: 38109374 DOI: 10.1146/annurev-biophys-030722-021957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.
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Affiliation(s)
| | - Julian Trouillon
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
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3
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Patel A, McGrosso D, Hefner Y, Campeau A, Sastry AV, Maurya S, Rychel K, Gonzalez DJ, Palsson BO. Proteome allocation is linked to transcriptional regulation through a modularized transcriptome. Nat Commun 2024; 15:5234. [PMID: 38898010 PMCID: PMC11187210 DOI: 10.1038/s41467-024-49231-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.
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Affiliation(s)
- Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Dominic McGrosso
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anaamika Campeau
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Svetlana Maurya
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - David J Gonzalez
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs, Lyngby, Denmark.
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4
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Ferreira MADM, Silveira WBD, Nikoloski Z. Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes. Biotechnol Bioeng 2024; 121:915-930. [PMID: 38178617 DOI: 10.1002/bit.28650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/24/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024]
Abstract
Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.
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Affiliation(s)
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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5
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Miao R, Jahn M, Shabestary K, Peltier G, Hudson EP. CRISPR interference screens reveal growth-robustness tradeoffs in Synechocystis sp. PCC 6803 across growth conditions. THE PLANT CELL 2023; 35:3937-3956. [PMID: 37494719 PMCID: PMC10615215 DOI: 10.1093/plcell/koad208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/01/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023]
Abstract
Barcoded mutant libraries are a powerful tool for elucidating gene function in microbes, particularly when screened in multiple growth conditions. Here, we screened a pooled CRISPR interference library of the model cyanobacterium Synechocystis sp. PCC 6803 in 11 bioreactor-controlled conditions, spanning multiple light regimes and carbon sources. This gene repression library contained 21,705 individual mutants with high redundancy over all open reading frames and noncoding RNAs. Comparison of the derived gene fitness scores revealed multiple instances of gene repression being beneficial in 1 condition while generally detrimental in others, particularly for genes within light harvesting and conversion, such as antennae components at high light and PSII subunits during photoheterotrophy. Suboptimal regulation of such genes likely represents a tradeoff of reduced growth speed for enhanced robustness to perturbation. The extensive data set assigns condition-specific importance to many previously unannotated genes and suggests additional functions for central metabolic enzymes. Phosphoribulokinase, glyceraldehyde-3-phosphate dehydrogenase, and the small protein CP12 were critical for mixotrophy and photoheterotrophy, which implicates the ternary complex as important for redirecting metabolic flux in these conditions in addition to inactivation of the Calvin cycle in the dark. To predict the potency of sgRNA sequences, we applied machine learning on sgRNA sequences and gene repression data, which showed the importance of C enrichment and T depletion proximal to the PAM site. Fitness data for all genes in all conditions are compiled in an interactive web application.
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Affiliation(s)
- Rui Miao
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, SE-17165,Sweden
| | - Michael Jahn
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, SE-17165,Sweden
- Max Planck Unit for the Science of Pathogens, 10117 Berlin,Germany
| | - Kiyan Shabestary
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, SE-17165,Sweden
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ,UK
| | - Gilles Peltier
- Aix Marseille Univ, CEA, CNRS, Institut de Biosciences et Biotechnologies Aix-Marseille, CEA Cadarache, 13108 Saint Paul-Lez-Durance,France
| | - Elton P Hudson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, SE-17165,Sweden
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6
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Hu XP, Schroeder S, Lercher MJ. Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow. mSystems 2023; 8:e0076023. [PMID: 37795991 PMCID: PMC10654084 DOI: 10.1128/msystems.00760-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
IMPORTANCE Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.
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Affiliation(s)
- Xiao-Pan Hu
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Stefan Schroeder
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Martin J. Lercher
- Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Heinrich Heine University, Düsseldorf, Germany
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7
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Ferreira MADM, da Silveira WB, Nikoloski Z. PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. PLoS Comput Biol 2023; 19:e1011549. [PMID: 37856550 PMCID: PMC10617714 DOI: 10.1371/journal.pcbi.1011549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/31/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. To this end, PARROT variants model the minimization of enzyme reallocation using four different (combinations of) distance functions. We demonstrate that the PARROT variant that minimizes the Manhattan distance between the enzyme allocation of a reference and an alternative condition outperforms existing approaches based on the parsimonious distribution of fluxes or enzymes for both Escherichia coli and Saccharomyces cerevisiae. Further, we show that the combined minimization of flux and enzyme allocation adjustment leads to inconsistent predictions. Together, our findings indicate that minimization of protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions.
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Affiliation(s)
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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8
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Balakrishnan R, Cremer J. Conditionally unutilized proteins and their profound effects on growth and adaptation across microbial species. Curr Opin Microbiol 2023; 75:102366. [PMID: 37625262 DOI: 10.1016/j.mib.2023.102366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/12/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023]
Abstract
Protein synthesis is an important determinant of microbial growth and response that demands a high amount of metabolic and biosynthetic resources. Despite these costs, microbial species from different taxa and habitats massively synthesize proteins that are not utilized in the conditions they currently experience. Based on resource allocation models, recent studies have begun to reconcile the costs and benefits of these conditionally unutilized proteins (CUPs) in the context of varying environmental conditions. Such massive synthesis of CUPs is crucial to consider in different areas of modern microbiology, from the systematic investigation of cell physiology, via the prediction of evolution in laboratory and natural environments, to the rational design of strains in biotechnology applications.
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Affiliation(s)
- Rohan Balakrishnan
- Department of Physics, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
| | - Jonas Cremer
- Department of Biology, Stanford University, 318 Campus Drive, Stanford, CA 93105, USA.
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9
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Bruggeman FJ, Teusink B, Steuer R. Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models. Bioessays 2023; 45:e2300015. [PMID: 37559168 DOI: 10.1002/bies.202300015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023]
Abstract
Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.
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Affiliation(s)
- Frank J Bruggeman
- Systems Biology Lab/AIMMS, VU University, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Biology Lab/AIMMS, VU University, Amsterdam, The Netherlands
| | - Ralf Steuer
- Institute for Theoretical Biology (ITB), Institute for Biology, Humboldt-University of Berlin, Berlin, Germany
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10
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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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11
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Mori M, Cheng C, Taylor BR, Okano H, Hwa T. Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions. Nat Commun 2023; 14:4161. [PMID: 37443156 PMCID: PMC10345195 DOI: 10.1038/s41467-023-39724-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.
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Affiliation(s)
- Matteo Mori
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA.
| | - Chuankai Cheng
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Brian R Taylor
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
| | - Hiroyuki Okano
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
| | - Terence Hwa
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
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12
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Lewis J, Scott NE. CRISPRi-Mediated Silencing of Burkholderia O-Linked Glycosylation Systems Enables the Depletion of Glycosylation Yet Results in Modest Proteome Impacts. J Proteome Res 2023; 22:1762-1778. [PMID: 36995114 PMCID: PMC10243306 DOI: 10.1021/acs.jproteome.2c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Indexed: 03/31/2023]
Abstract
The process of O-linked protein glycosylation is highly conserved across the Burkholderia genus and mediated by the oligosaccharyltransferase PglL. While our understanding of Burkholderia glycoproteomes has increased in recent years, little is known about how Burkholderia species respond to modulations in glycosylation. Utilizing CRISPR interference (CRISPRi), we explored the impact of silencing of O-linked glycosylation across four species of Burkholderia; Burkholderia cenocepacia K56-2, Burkholderia diffusa MSMB375, Burkholderia multivorans ATCC17616, and Burkholderia thailandensis E264. Proteomic and glycoproteomic analyses revealed that while CRISPRi enabled inducible silencing of PglL, this did not abolish glycosylation, nor recapitulate phenotypes such as proteome changes or alterations in motility that are associated with glycosylation null strains, despite inhibition of glycosylation by nearly 90%. Importantly, this work also demonstrated that CRISPRi induction with high levels of rhamnose leads to extensive impacts on the Burkholderia proteomes, which without appropriate controls mask the impacts specifically driven by CRISPRi guides. Combined, this work revealed that while CRISPRi allows the modulation of O-linked glycosylation with reductions up to 90% at a phenotypic and proteome levels, Burkholderia appears to demonstrate a robust tolerance to fluctuations in glycosylation capacity.
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Affiliation(s)
- Jessica
M. Lewis
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute
for Infection and Immunity, Melbourne 3000, Australia
| | - Nichollas E. Scott
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute
for Infection and Immunity, Melbourne 3000, Australia
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13
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Wilkes RA, Waldbauer J, Carroll A, Nieto-Domínguez M, Parker DJ, Zhang L, Guss AM, Aristilde L. Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism. Nat Chem Biol 2023; 19:651-662. [PMID: 36747056 PMCID: PMC10154247 DOI: 10.1038/s41589-022-01237-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 11/29/2022] [Indexed: 02/08/2023]
Abstract
Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.
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Affiliation(s)
- Rebecca A Wilkes
- Department of Biological and Environmental Engineering, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
- Department of Civil and Environmental Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL, USA
| | - Jacob Waldbauer
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
| | - Austin Carroll
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Manuel Nieto-Domínguez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Darren J Parker
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Lichun Zhang
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
| | - Adam M Guss
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ludmilla Aristilde
- Department of Biological and Environmental Engineering, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA.
- Department of Civil and Environmental Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL, USA.
- Northwestern Center for Synthetic Biology, Evanston, IL, USA.
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14
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Patel A, McGrosso D, Hefner Y, Campeau A, Sastry AV, Maurya S, Rychel K, Gonzalez DJ, Palsson BO. Proteome allocation is linked to transcriptional regulation through a modularized transcriptome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529291. [PMID: 36865326 PMCID: PMC9980150 DOI: 10.1101/2023.02.20.529291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigated whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions could be modularized in the same way to reveal novel relationships between their compositions. We found that; 1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, 2) the modules in the proteome often represent combinations of modules from the transcriptome, 3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and 4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.
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Affiliation(s)
- Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Dominic McGrosso
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anaamika Campeau
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand V. Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Svetlana Maurya
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - David J Gonzalez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
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15
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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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16
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Schito S, Zuchowski R, Bergen D, Strohmeier D, Wollenhaupt B, Menke P, Seiffarth J, Nöh K, Kohlheyer D, Bott M, Wiechert W, Baumgart M, Noack S. Communities of Niche-optimized Strains (CoNoS) - Design and creation of stable, genome-reduced co-cultures. Metab Eng 2022; 73:91-103. [PMID: 35750243 DOI: 10.1016/j.ymben.2022.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/20/2022] [Accepted: 06/17/2022] [Indexed: 10/18/2022]
Abstract
Current bioprocesses for production of value-added compounds are mainly based on pure cultures that are composed of rationally engineered strains of model organisms with versatile metabolic capacities. However, in the comparably well-defined environment of a bioreactor, metabolic flexibility provided by various highly abundant biosynthetic enzymes is much less required and results in suboptimal use of carbon and energy sources for compound production. In nature, non-model organisms have frequently evolved in communities where genome-reduced, auxotrophic strains cross-feed each other, suggesting that there must be a significant advantage compared to growth without cooperation. To prove this, we started to create and study synthetic communities of niche-optimized strains (CoNoS) that consists of two strains of the same species Corynebacterium glutamicum that are mutually dependent on one amino acid. We used both the wild-type and the genome-reduced C1* chassis for introducing selected amino acid auxotrophies, each based on complete deletion of all required biosynthetic genes. The best candidate strains were used to establish several stably growing CoNoS that were further characterized and optimized by metabolic modelling, microfluidic experiments and rational metabolic engineering to improve amino acid production and exchange. Finally, the engineered CoNoS consisting of an l-leucine and l-arginine auxotroph showed a specific growth rate equivalent to 83% of the wild type in monoculture, making it the fastest co-culture of two auxotrophic C. glutamicum strains to date. Overall, our results are a first promising step towards establishing improved biobased production of value-added compounds using the CoNoS approach.
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Affiliation(s)
- Simone Schito
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Rico Zuchowski
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Bergen
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Strohmeier
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Bastian Wollenhaupt
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Philipp Menke
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Johannes Seiffarth
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Katharina Nöh
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Dietrich Kohlheyer
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Michael Bott
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Wolfgang Wiechert
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, D-52074, Aachen, Germany
| | - Meike Baumgart
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Stephan Noack
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany.
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17
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Kleijn IT, Martínez-Segura A, Bertaux F, Saint M, Kramer H, Shahrezaei V, Marguerat S. Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast. Life Sci Alliance 2022; 5:5/5/e202101223. [PMID: 35228260 PMCID: PMC8886410 DOI: 10.26508/lsa.202101223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/20/2022] Open
Abstract
Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression.
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Affiliation(s)
- Istvan T Kleijn
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK,Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK
| | - Amalia Martínez-Segura
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK
| | - François Bertaux
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK,Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK
| | - Malika Saint
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK
| | - Holger Kramer
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK
| | - Samuel Marguerat
- Medical Research Council London Institute of Medical Sciences (MRC LMS), London, UK .,Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK
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18
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Elsemman IE, Rodriguez Prado A, Grigaitis P, Garcia Albornoz M, Harman V, Holman SW, van Heerden J, Bruggeman FJ, Bisschops MMM, Sonnenschein N, Hubbard S, Beynon R, Daran-Lapujade P, Nielsen J, Teusink B. Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies. Nat Commun 2022; 13:801. [PMID: 35145105 PMCID: PMC8831649 DOI: 10.1038/s41467-022-28467-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/27/2022] [Indexed: 01/20/2023] Open
Abstract
When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells. Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies.
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Affiliation(s)
- Ibrahim E Elsemman
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark.,Department of Information Systems, Faculty of Computers and Information, Assiut University, Assiut, Egypt
| | - Angelica Rodriguez Prado
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Pranas Grigaitis
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Victoria Harman
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Stephen W Holman
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Johan van Heerden
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank J Bruggeman
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark M M Bisschops
- Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Nikolaus Sonnenschein
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark
| | - Simon Hubbard
- Division of Evolution & Genomic Sciences, University of Manchester, Manchester, UK
| | - Rob Beynon
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Pascale Daran-Lapujade
- Department of Industrial Microbiology, Technical University Delft, Delft, The Netherlands
| | - Jens Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark. .,Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden.
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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19
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Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth. PLoS Comput Biol 2022; 18:e1009843. [PMID: 35104290 PMCID: PMC8853647 DOI: 10.1371/journal.pcbi.1009843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 02/17/2022] [Accepted: 01/18/2022] [Indexed: 11/19/2022] Open
Abstract
Traditional (genome-scale) metabolic models of cellular growth involve an approximate biomass “reaction”, which specifies biomass composition in terms of precursor metabolites (such as amino acids and nucleotides). On the one hand, biomass composition is often not known exactly and may vary drastically between conditions and strains. On the other hand, the predictions of computational models crucially depend on biomass. Also elementary flux modes (EFMs), which generate the flux cone, depend on the biomass reaction. To better understand cellular phenotypes across growth conditions, we introduce and analyze new classes of elementary vectors for comprehensive (next-generation) metabolic models, involving explicit synthesis reactions for all macromolecules. Elementary growth modes (EGMs) are given by stoichiometry and generate the growth cone. Unlike EFMs, they are not support-minimal, in general, but cannot be decomposed “without cancellations”. In models with additional (capacity) constraints, elementary growth vectors (EGVs) generate a growth polyhedron and depend also on growth rate. However, EGMs/EGVs do not depend on the biomass composition. In fact, they cover all possible biomass compositions and can be seen as unbiased versions of elementary flux modes/vectors (EFMs/EFVs) used in traditional models. To relate the new concepts to other branches of theory, we consider autocatalytic sets of reactions. Further, we illustrate our results in a small model of a self-fabricating cell, involving glucose and ammonium uptake, amino acid and lipid synthesis, and the expression of all enzymes and the ribosome itself. In particular, we study the variation of biomass composition as a function of growth rate. In agreement with experimental data, low nitrogen uptake correlates with high carbon (lipid) storage. Next-generation, genome-scale metabolic models allow to study the reallocation of cellular resources upon changing environmental conditions, by not only modeling flux distributions, but also expression profiles of the catalyzing proteome. In particular, they do no longer assume a fixed biomass composition. Methods to identify optimal solutions in such comprehensive models exist, however, an unbiased understanding of all feasible allocations is missing so far. Here we develop new concepts, called elementary growth modes and vectors, that provide a generalized definition of minimal pathways, thereby extending classical elementary flux modes (used in traditional models with a fixed biomass composition). The new concepts provide an understanding of all possible flux distributions and of all possible biomass compositions. In other words, elementary growth modes and vectors are the unique functional units in any comprehensive model of cellular growth. As an example, we show that lipid accumulation upon nitrogen starvation is a consequence of resource allocation and does not require active regulation. Our work puts current approaches on a theoretical basis and allows to seamlessly transfer existing workflows (e.g. for the design of cell factories) to next-generation metabolic models.
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20
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Pal A, Iyer MS, Srinivasan S, Narain Seshasayee AS, Venkatesh KV. Global pleiotropic effects in adaptively evolved Escherichia coli lacking CRP reveal molecular mechanisms that define the growth physiology. Open Biol 2022; 12:210206. [PMID: 35167766 PMCID: PMC8846999 DOI: 10.1098/rsob.210206] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Evolution facilitates emergence of fitter phenotypes by efficient allocation of cellular resources in conjunction with beneficial mutations. However, system-wide pleiotropic effects that redress the perturbations to the apex node of the transcriptional regulatory networks remain unclear. Here, we elucidate that absence of global transcriptional regulator CRP in Escherichia coli results in alterations in key metabolic pathways under glucose respiratory conditions, favouring stress- or hedging-related functions over growth-enhancing functions. Further, we disentangle the growth-mediated effects from the CRP regulation-specific effects on these metabolic pathways. We quantitatively illustrate that the loss of CRP perturbs proteome efficiency, as evident from metabolic as well as ribosomal proteome fractions, that corroborated with intracellular metabolite profiles. To address how E. coli copes with such systemic defect, we evolved Δcrp mutant in the presence of glucose. Besides acquiring mutations in the promoter of glucose transporter ptsG, the evolved populations recovered the metabolic pathways to their pre-perturbed state coupled with metabolite re-adjustments, which altogether enabled increased growth. By contrast to Δcrp mutant, the evolved strains remodelled their proteome efficiency towards biomass synthesis, albeit at the expense of carbon efficiency. Overall, we comprehensively illustrate the genetic and metabolic basis of pleiotropic effects, fundamental for understanding the growth physiology.
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Affiliation(s)
- Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Mahesh S. Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sumana Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | | | - K. V. Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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21
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Kamali Dolatabadi R, Feizi A, Halaji M, Fazeli H, Adibi P. The Prevalence of Adherent-Invasive Escherichia coli and Its Association With Inflammatory Bowel Diseases: A Systematic Review and Meta-Analysis. Front Med (Lausanne) 2021; 8:730243. [PMID: 34926490 PMCID: PMC8678049 DOI: 10.3389/fmed.2021.730243] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are known as chronic gastrointestinal inflammatory disorders. The present systematic review and meta analysis was conducted to estimate the prevalence of adherent-invasive Escherichia coli (AIEC) isolates and their phylogenetic grouping among IBD patients compared with the controls. A systematic literature search was conducted among published papers by international authors until April 30, 2020 in Web of Science, Scopus, EMBASE, and PubMed databases. The pooled prevalence of AIEC isolates and their phylogenetic grouping among IBD patients as well as in controls was estimated using fixed or random effects models. Furthermore, for estimating the association of colonization by AIEC with IBD, odds ratio along with 95% confidence interval was reported. A total of 205 articles retrieved by the initial search of databases, 13 case–control studies met the eligibility criteria for inclusion in the meta analysis. There were 465 IBD cases (348 CD and 117 UC) and 307 controls. The pooled prevalence of AIEC isolates were 28% (95% CI: 18–39%), 29% (95% CI: 20–40%), 13% (95% CI: 1–30%), and 9% (95% CI: 3–19%), respectively among IBD, CD, UC, and control group, respectively. Our results revealed that the most frequent AIEC phylogroup in the IBD, CD, and control groups was B2. Fixed-effects meta analysis showed that colonization of AIEC is significantly associated with IBD (OR: 2.93; 95% CI: 1.90–4.52; P < 0.001) and CD (OR: 3.07; 95% CI: 1.99–4.74; P < 0.001), but not with UC (OR: 2.29; 95% CI: 0.81–6.51; P = 0.11). In summary, this meta analysis revealed that colonization by AIEC is more frequent in IBD and is associated with IBD (CD and UC). Our results suggested that the affects of IBD in patients colonized with the AIEC pathovar is not random, it is in fact a specific disease-related pathovar.
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Affiliation(s)
- Razie Kamali Dolatabadi
- Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Awat Feizi
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehrdad Halaji
- Infectious Diseases and Tropical Medicine Research Center, Babol University of Medical Sciences, Babol, Iran.,Department of Microbiology, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Hossein Fazeli
- Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Peyman Adibi
- Gastroenterology and Hepatology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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22
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Balakrishnan R, de Silva RT, Hwa T, Cremer J. Suboptimal resource allocation in changing environments constrains response and growth in bacteria. Mol Syst Biol 2021; 17:e10597. [PMID: 34928547 PMCID: PMC8687047 DOI: 10.15252/msb.202110597] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/29/2021] [Accepted: 12/01/2021] [Indexed: 11/13/2022] Open
Abstract
To respond to fluctuating conditions, microbes typically need to synthesize novel proteins. As this synthesis relies on sufficient biosynthetic precursors, microbes must devise effective response strategies to manage depleting precursors. To better understand these strategies, we investigate the active response of Escherichia coli to changes in nutrient conditions, connecting transient gene expression to growth phenotypes. By synthetically modifying gene expression during changing conditions, we show how the competition by genes for the limited protein synthesis capacity constrains cellular response. Despite this constraint cells substantially express genes that are not required, trapping them in states where precursor levels are low and the genes needed to replenish the precursors are outcompeted. Contrary to common modeling assumptions, our findings highlight that cells do not optimize growth under changing environments but rather exhibit hardwired response strategies that may have evolved to promote fitness in their native environment. The constraint and the suboptimality of the cellular response uncovered provide a conceptual framework relevant for many research applications, from the prediction of evolution to the improvement of gene circuits in biotechnology.
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Affiliation(s)
| | | | - Terence Hwa
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
- Division of Biological SciencesUniversity of California at San DiegoLa JollaCAUSA
| | - Jonas Cremer
- Department of BiologyStanford UniversityStanfordCAUSA
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23
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Jahn M, Crang N, Janasch M, Hober A, Forsström B, Kimler K, Mattausch A, Chen Q, Asplund-Samuelsson J, Hudson EP. Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator. eLife 2021; 10:69019. [PMID: 34723797 PMCID: PMC8591527 DOI: 10.7554/elife.69019] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/30/2021] [Indexed: 12/12/2022] Open
Abstract
Bacteria must balance the different needs for substrate assimilation, growth
functions, and resilience in order to thrive in their environment. Of all
cellular macromolecules, the bacterial proteome is by far the most important
resource and its size is limited. Here, we investigated how the highly versatile
'knallgas' bacterium Cupriavidus necator reallocates protein
resources when grown on different limiting substrates and with different growth
rates. We determined protein quantity by mass spectrometry and estimated enzyme
utilization by resource balance analysis modeling. We found that C.
necator invests a large fraction of its proteome in functions that
are hardly utilized. Of the enzymes that are utilized, many are present in
excess abundance. One prominent example is the strong expression of CBB cycle
genes such as Rubisco during growth on fructose. Modeling and mutant competition
experiments suggest that CO2-reassimilation through Rubisco does not
provide a fitness benefit for heterotrophic growth, but is rather an investment
in readiness for autotrophy.
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Affiliation(s)
- Michael Jahn
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Nick Crang
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Markus Janasch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Andreas Hober
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Björn Forsström
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Kyle Kimler
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Alexander Mattausch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Qi Chen
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Johannes Asplund-Samuelsson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Elton Paul Hudson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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24
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Zeng H, Rohani R, Huang WE, Yang A. Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis. BMC Bioinformatics 2021; 22:467. [PMID: 34583645 PMCID: PMC8479906 DOI: 10.1186/s12859-021-04382-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 09/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings. RESULTS In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology. CONCLUSIONS We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.
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Affiliation(s)
- Hong Zeng
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Reza Rohani
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
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25
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Abstract
Metal ions are essential to all living cells, as they can serve as cofactors of enzymes to drive catalysis of biochemical reactions. We present a constraint-based model of yeast that relates metabolism with metal ions via enzymes. The model is able to capture responses of metabolism and gene expression upon iron depletion, suggesting that yeast cells allocate iron resource in the way abiding to optimization principles. Interestingly, the model predicts up-regulation of several iron-containing enzymes that coincide with experiments, which raises the possibility that the decrease in activity due to limited iron could be compensated by elevated enzyme abundance. Moreover, the model paves the way for guiding biosynthesis of high-value compounds (e.g., p-coumaric acid) that relies on iron-containing enzymes. Metal ions are vital to metabolism, as they can act as cofactors on enzymes and thus modulate individual enzymatic reactions. Although many enzymes have been reported to interact with metal ions, the quantitative relationships between metal ions and metabolism are lacking. Here, we reconstructed a genome-scale metabolic model of the yeast Saccharomyces cerevisiae to account for proteome constraints and enzyme cofactors such as metal ions, named CofactorYeast. The model is able to estimate abundances of metal ions binding on enzymes in cells under various conditions, which are comparable to measured metal ion contents in biomass. In addition, the model predicts distinct metabolic flux distributions in response to reduced levels of various metal ions in the medium. Specifically, the model reproduces changes upon iron deficiency in metabolic and gene expression levels, which could be interpreted by optimization principles (i.e., yeast optimizes iron utilization based on metabolic network and enzyme kinetics rather than preferentially targeting iron to specific enzymes or pathways). At last, we show the potential of using the model for understanding cell factories that harbor heterologous iron-containing enzymes to synthesize high-value compounds such as p-coumaric acid. Overall, the model demonstrates the dependence of enzymes on metal ions and links metal ions to metabolism on a genome scale.
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Abstract
Turnover numbers (kcat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (kapp values) in bacteria, a large-scale estimation of kapp in eukaryotes is lacking. Here, we estimated kapp of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum kapp across conditions with in vitro kcat we found a weak correlation in log scale of R2 = 0.28, which is lower than for Escherichia coli (R2 = 0.62). The weak correlation is caused by the fact that many in vitro kcat values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to R2 = 0.41 but still not as good as for E. coli, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our kapp dataset we observed better performance than the default model with in vitro kcat in predicting proteomics data, demonstrating the strength of using the dataset generated here.
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The effect of natural selection on the propagation of protein expression noise to bacterial growth. PLoS Comput Biol 2021; 17:e1009208. [PMID: 34280182 PMCID: PMC8321134 DOI: 10.1371/journal.pcbi.1009208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 07/29/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
In bacterial cells, protein expression is a highly stochastic process. Gene expression noise moreover propagates through the cell and adds to fluctuations in the cellular growth rate. A common intuition is that, due to their relatively high noise amplitudes, proteins with a low mean expression level are the most important drivers of fluctuations in physiological variables. In this work, we challenge this intuition by considering the effect of natural selection on noise propagation. Mathematically, the contribution of each protein species to the noise in the growth rate depends on two factors: the noise amplitude of the protein’s expression level, and the sensitivity of the growth rate to fluctuations in that protein’s concentration. We argue that natural selection, while shaping mean abundances to increase the mean growth rate, also affects cellular sensitivities. In the limit in which cells grow optimally fast, the growth rate becomes most sensitive to fluctuations in highly abundant proteins. This causes abundant proteins to overall contribute strongly to the noise in the growth rate, despite their low noise levels. We further explore this result in an experimental data set of protein abundances, and test key assumptions in an evolving, stochastic toy model of cellular growth. Gene expression in bacterial cells is intrinsically stochastic: copy numbers of all proteins vary between genetically identical cells, even in a homogeneous environment. Such noise in gene expression affects metabolic fluxes and even propagates to the cellular level. Indeed, also the growth rate of individual cells, an important proxy for fitness, fluctuates strongly. This beckons the question as to which protein species contribute most to the noise in the cell’s growth rate. We here derive general mathematical predictions stating that if cells have been optimised by evolution to grow fast, abundant protein species contribute most to the noise in the growth rate. This result is counter-intuitive, because the noise levels in the expression of those highly expressed proteins are small.
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Grekov I, Thöming JG, Kordes A, Häussler S. Evolution of Pseudomonas aeruginosa toward higher fitness under standard laboratory conditions. THE ISME JOURNAL 2021; 15:1165-1177. [PMID: 33273720 PMCID: PMC8115180 DOI: 10.1038/s41396-020-00841-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/04/2020] [Accepted: 11/11/2020] [Indexed: 01/29/2023]
Abstract
Identifying genetic factors that contribute to the evolution of adaptive phenotypes in pathogenic bacteria is key to understanding the establishment of infectious diseases. In this study, we performed mutation accumulation experiments to record the frequency of mutations and their effect on fitness in hypermutator strains of the environmental bacterium Pseudomonas aeruginosa in comparison to the host-niche-adapted Salmonella enterica. We demonstrate that P. aeruginosa, but not S. enterica, hypermutators evolve toward higher fitness under planktonic conditions. Adaptation to increased growth performance was accompanied by a reversible perturbing of the local genetic context of membrane and cell wall biosynthesis genes. Furthermore, we observed a fine-tuning of complex regulatory circuits involving multiple di-guanylate modulating enzymes that regulate the transition between fast growing planktonic and sessile biofilm-associated lifestyles. The redundancy and local specificity of the di-guanylate signaling pathways seem to allow a convergent shift toward increased growth performance across niche-adapted clonal P. aeruginosa lineages, which is accompanied by a pronounced heterogeneity of their motility, virulence, and biofilm phenotypes.
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Affiliation(s)
- Igor Grekov
- grid.7490.a0000 0001 2238 295XDepartment of Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany ,grid.475435.4Department of Clinical Microbiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Janne Gesine Thöming
- grid.452370.70000 0004 0408 1805Institute of Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany ,grid.475435.4Department of Clinical Microbiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Adrian Kordes
- grid.452370.70000 0004 0408 1805Institute of Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany ,grid.10423.340000 0000 9529 9877Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
| | - Susanne Häussler
- grid.7490.a0000 0001 2238 295XDepartment of Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany ,grid.452370.70000 0004 0408 1805Institute of Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany ,grid.475435.4Department of Clinical Microbiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark ,grid.10423.340000 0000 9529 9877Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
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29
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Kochanowski K, Okano H, Patsalo V, Williamson J, Sauer U, Hwa T. Global coordination of metabolic pathways in Escherichia coli by active and passive regulation. Mol Syst Biol 2021; 17:e10064. [PMID: 33852189 PMCID: PMC8045939 DOI: 10.15252/msb.202010064] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/15/2022] Open
Abstract
Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.
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Affiliation(s)
- Karl Kochanowski
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Life Science Zurich PhD Program on Systems BiologyZurichSwitzerland
| | - Hiroyuki Okano
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
| | - Vadim Patsalo
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - James Williamson
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical BiologyThe Scripps Research InstituteLa JollaCAUSA
| | - Uwe Sauer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Terence Hwa
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
- Institute for Theoretical ScienceETH ZurichZurichSwitzerland
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30
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Abstract
It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes. IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today’s whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.
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31
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Tourigny DS. Cooperative metabolic resource allocation in spatially-structured systems. J Math Biol 2021; 82:5. [PMID: 33479850 DOI: 10.1007/s00285-021-01558-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 06/30/2020] [Accepted: 10/27/2020] [Indexed: 10/22/2022]
Abstract
Natural selection has shaped the evolution of cells and multi-cellular organisms such that social cooperation can often be preferred over an individualistic approach to metabolic regulation. This paper extends a framework for dynamic metabolic resource allocation based on the maximum entropy principle to spatiotemporal models of metabolism with cooperation. Much like the maximum entropy principle encapsulates 'bet-hedging' behaviour displayed by organisms dealing with future uncertainty in a fluctuating environment, its cooperative extension describes how individuals adapt their metabolic resource allocation strategy to further accommodate limited knowledge about the welfare of others within a community. The resulting theory explains why local regulation of metabolic cross-feeding can fulfil a community-wide metabolic objective if individuals take into consideration an ensemble measure of total population performance as the only form of global information. The latter is likely supplied by quorum sensing in microbial systems or signalling molecules such as hormones in multi-cellular eukaryotic organisms.
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Affiliation(s)
- David S Tourigny
- Columbia University Irving Medical Center, 630 West 168th Street, New York, NY, 10032, USA.
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32
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Donati S, Kuntz M, Pahl V, Farke N, Beuter D, Glatter T, Gomes-Filho JV, Randau L, Wang CY, Link H. Multi-omics Analysis of CRISPRi-Knockdowns Identifies Mechanisms that Buffer Decreases of Enzymes in E. coli Metabolism. Cell Syst 2020; 12:56-67.e6. [PMID: 33238135 DOI: 10.1016/j.cels.2020.10.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/23/2020] [Accepted: 10/29/2020] [Indexed: 12/21/2022]
Abstract
Enzymes maintain metabolism, and their concentration affects cellular fitness: high enzyme levels are costly, and low enzyme levels can limit metabolic flux. Here, we used CRISPR interference (CRISPRi) to study the consequences of decreasing E. coli enzymes below wild-type levels. A pooled CRISPRi screen with 7,177 strains demonstrates that metabolism buffers fitness defects for hours after the induction of CRISPRi. We characterized the metabolome and proteome responses in 30 CRISPRi strains and elucidated three gene-specific buffering mechanisms: ornithine buffered the knockdown of carbamoyl phosphate synthetase (CarAB) by increasing CarAB activity, S-adenosylmethionine buffered the knockdown of homocysteine transmethylase (MetE) by de-repressing expression of the methionine pathway, and 6-phosphogluconate buffered the knockdown of 6-phosphogluconate dehydrogenase (Gnd) by activating a bypass. In total, this work demonstrates that CRISPRi screens can reveal global sources of metabolic robustness and identify local regulatory mechanisms that buffer decreases of specific enzymes. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Stefano Donati
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Vanessa Pahl
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Dominik Beuter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Lennart Randau
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Chun-Ying Wang
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
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33
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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34
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Proteome reallocation from amino acid biosynthesis to ribosomes enables yeast to grow faster in rich media. Proc Natl Acad Sci U S A 2020; 117:21804-21812. [PMID: 32817546 PMCID: PMC7474676 DOI: 10.1073/pnas.1921890117] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The yeast Saccharomyces cerevisiae is a well-studied organism, which is used as a model organism for studying eukaryal biology and as a cell factory for the production of fuels, chemicals, and pharmaceuticals. For both applications, the way that the cell utilizes its finite protein resource and how those inherent trade-offs manifest themselves is of interest, not least for their impact on cellular metabolism. Here we elucidate how alterations of protein-allocation allow for S. cerevisiae to increase its growth rate. Our results on cellular proteome-allocation may aid the engineering of more efficient strains in industrial biotechnology as well as improve our understanding toward phenotypes of cancer cells that grow faster than normal cells. Several recent studies have shown that the concept of proteome constraint, i.e., the need for the cell to balance allocation of its proteome between different cellular processes, is essential for ensuring proper cell function. However, there have been no attempts to elucidate how cells’ maximum capacity to grow depends on protein availability for different cellular processes. To experimentally address this, we cultivated Saccharomyces cerevisiae in bioreactors with or without amino acid supplementation and performed quantitative proteomics to analyze global changes in proteome allocation, during both anaerobic and aerobic growth on glucose. Analysis of the proteomic data implies that proteome mass is mainly reallocated from amino acid biosynthetic processes into translation, which enables an increased growth rate during supplementation. Similar findings were obtained from both aerobic and anaerobic cultivations. Our findings show that cells can increase their growth rate through increasing its proteome allocation toward the protein translational machinery.
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35
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Parker DJ, Lalanne JB, Kimura S, Johnson GE, Waldor MK, Li GW. Growth-Optimized Aminoacyl-tRNA Synthetase Levels Prevent Maximal tRNA Charging. Cell Syst 2020; 11:121-130.e6. [PMID: 32726597 DOI: 10.1016/j.cels.2020.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 05/07/2020] [Accepted: 07/02/2020] [Indexed: 01/28/2023]
Abstract
Aminoacyl-tRNA synthetases (aaRSs) serve a dual role in charging tRNAs. Their enzymatic activities both provide protein synthesis flux and reduce uncharged tRNA levels. Although uncharged tRNAs can negatively impact bacterial growth, substantial concentrations of tRNAs remain deacylated even under nutrient-rich conditions. Here, we show that tRNA charging in Bacillus subtilis is not maximized due to optimization of aaRS production during rapid growth, which prioritizes demands in protein synthesis over charging levels. The presence of uncharged tRNAs is alleviated by precisely tuned translation kinetics and the stringent response, both insensitive to aaRS overproduction but sharply responsive to underproduction, allowing for just enough aaRS production atop a "fitness cliff." Notably, we find that the stringent response mitigates fitness defects at all aaRS underproduction levels even without external starvation. Thus, adherence to minimal, flux-satisfying protein production drives limited tRNA charging and provides a basis for the sensitivity and setpoints of an integrated growth-control network.
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Affiliation(s)
- Darren J Parker
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jean-Benoît Lalanne
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Satoshi Kimura
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Grace E Johnson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Matthew K Waldor
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Gene-Wei Li
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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36
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Basan M, Honda T, Christodoulou D, Hörl M, Chang YF, Leoncini E, Mukherjee A, Okano H, Taylor BR, Silverman JM, Sanchez C, Williamson JR, Paulsson J, Hwa T, Sauer U. A universal trade-off between growth and lag in fluctuating environments. Nature 2020; 584:470-474. [PMID: 32669712 PMCID: PMC7442741 DOI: 10.1038/s41586-020-2505-4] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 04/21/2020] [Indexed: 12/01/2022]
Abstract
The rate of cell growth is crucial for bacterial fitness and a main driver of proteome allocation1,2, but it is unclear what ultimately determines growth rates in different environmental conditions. Increasing evidence suggests that other objectives also play key roles3–7, such as the rate of physiological adaptation to changing environments8,9. The challenge for cells is that these objectives often cannot be independently optimized, and maximizing one often reduces another. Many such tradeoffs have indeed been hypothesized, based on qualitative correlative studies8–11. Here we report the occurrence of a tradeoff between steady-state growth rate and physiological adaptability for Escherichia coli, upon abruptly shifting a growing culture from a preferred carbon source to fermentation products such as acetate. Such transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis revealed that the long lags resulted from the depletion of key metabolites due to the sudden reversal of central carbon flux imposed by these nutrient shifts. A model of sequential flux limitation not only explained the observed universal tradeoff between growth and adaptability, but also generated quantitative predictions that were validated experimentally. The observed trade-off reflects the opposing enzyme requirements for glycolysis versus gluconeogenesis.
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Affiliation(s)
- Markus Basan
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA. .,Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
| | - Tomoya Honda
- Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA
| | | | - Manuel Hörl
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Yu-Fang Chang
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Emanuele Leoncini
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Avik Mukherjee
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hiroyuki Okano
- Department of Physics, University of California at San Diego, La Jolla, CA, USA
| | - Brian R Taylor
- Department of Physics, University of California at San Diego, La Jolla, CA, USA
| | - Josh M Silverman
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Carlos Sanchez
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - James R Williamson
- Department of Integrative Structural and Computational Biology, and The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Johan Paulsson
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Terence Hwa
- Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA. .,Department of Physics, University of California at San Diego, La Jolla, CA, USA.
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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37
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A quantitative method for proteome reallocation using minimal regulatory interventions. Nat Chem Biol 2020; 16:1026-1033. [PMID: 32661378 DOI: 10.1038/s41589-020-0593-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 06/15/2020] [Indexed: 12/22/2022]
Abstract
Engineering resource allocation in biological systems is an ongoing challenge. Organisms allocate resources for ensuring survival, reducing the productivity of synthetic biology functions. Here we present a new approach for engineering the resource allocation of Escherichia coli by rationally modifying its transcriptional regulatory network. Our method (ReProMin) identifies the minimal set of genetic interventions that maximizes the savings in cell resources. To this end, we categorized transcription factors according to the essentiality of its targets and we used proteomic data to rank them. We designed the combinatorial removal of transcription factors that maximize the release of resources. Our resulting strain containing only three mutations, theoretically releasing 0.5% of its proteome, had higher proteome budget, increased production of an engineered metabolic pathway and showed that the regulatory interventions are highly specific. This approach shows that combining proteomic and regulatory data is an effective way of optimizing strains using conventional molecular methods.
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38
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Tourigny DS. Dynamic metabolic resource allocation based on the maximum entropy principle. J Math Biol 2020; 80:2395-2430. [PMID: 32424475 DOI: 10.1007/s00285-020-01499-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 03/08/2020] [Indexed: 01/06/2023]
Abstract
Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.
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Affiliation(s)
- David S Tourigny
- Columbia University Irving Medical Center, 630 West 168th Street, New York, NY, 10032, USA.
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39
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Bacterial Glycogen Provides Short-Term Benefits in Changing Environments. Appl Environ Microbiol 2020; 86:AEM.00049-20. [PMID: 32111592 DOI: 10.1128/aem.00049-20] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 02/26/2020] [Indexed: 11/20/2022] Open
Abstract
Changing nutritional conditions challenge microbes and shape their evolutionary optimization. Here, we used real-time metabolomics to investigate the role of glycogen in the dynamic physiological adaptation of Escherichia coli to fluctuating nutrients following carbon starvation. After the depletion of environmental glucose, we found significant metabolic activity remaining, which was linked to rapid utilization of intracellular glycogen. Glycogen was depleted by 80% within minutes of glucose starvation and was similarly replenished within minutes of glucose availability. These fast time scales of glycogen utilization correspond to the short-term benefits that glycogen provided to cells undergoing various physiological transitions. Cells capable of utilizing glycogen exhibited shorter lag times than glycogen mutants when starved between periods of exposure to different carbon sources. The ability to utilize glycogen was also important for the transition between planktonic and biofilm lifestyles and enabled increased glucose uptake during pulses of limited glucose availability. While wild-type and mutant strains exhibited comparable growth rates in steady environments, mutants deficient in glycogen utilization grew more poorly in environments that fluctuated on minute scales between carbon availability and starvation. Taken together, these results highlight an underappreciated role of glycogen in rapidly providing carbon and energy in changing environments, thereby increasing survival and competition capabilities under fluctuating and nutrient-poor conditions.IMPORTANCE Nothing is constant in life, and microbes in particular have to adapt to frequent and rapid environmental changes. Here, we used real-time metabolomics and single-cell imaging to demonstrate that the internal storage polymer glycogen plays a crucial role in such dynamic adaptations. Glycogen is depleted within minutes of glucose starvation and similarly is replenished within minutes of glucose availability. Cells capable of utilizing glycogen exhibited shorter lag times than glycogen mutants when starved between periods of exposure to different carbon sources. While wild-type and mutant strains exhibited comparable growth rates in steady environments, mutants deficient in glycogen utilization grew more poorly in environments that fluctuated on minute scales between carbon availability and starvation. These results highlight an underappreciated role of glycogen in rapidly providing carbon and energy in changing environments, thereby increasing survival and competition capabilities under fluctuating and nutrient-poor conditions.
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40
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Abstract
The biological fitness of microbes is largely determined by the rate with which they replicate their biomass composition. Mathematical models that maximize this balanced growth rate while accounting for mass conservation, reaction kinetics, and limits on dry mass per volume are inevitably non-linear. Here, we develop a general theory for such models, termed Growth Balance Analysis (GBA), which provides explicit expressions for protein concentrations, fluxes, and growth rates. These variables are functions of the concentrations of cellular components, for which we calculate marginal fitness costs and benefits that are related to metabolic control coefficients. At maximal growth rate, the net benefits of all concentrations are equal. Based solely on physicochemical constraints, GBA unveils fundamental quantitative principles of cellular resource allocation and growth; it accurately predicts the relationship between growth rates and ribosome concentrations in E. coli and yeast and between growth rate and dry mass density in E. coli.
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Affiliation(s)
- Hugo Dourado
- Institute for Computer Science & Department of Biology, Heinrich Heine University, 40221, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science & Department of Biology, Heinrich Heine University, 40221, Düsseldorf, Germany.
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de Groot DH, Hulshof J, Teusink B, Bruggeman FJ, Planqué R. Elementary Growth Modes provide a molecular description of cellular self-fabrication. PLoS Comput Biol 2020; 16:e1007559. [PMID: 31986156 PMCID: PMC7004393 DOI: 10.1371/journal.pcbi.1007559] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 02/06/2020] [Accepted: 11/22/2019] [Indexed: 02/04/2023] Open
Abstract
In this paper we try to describe all possible molecular states (phenotypes) for a cell that fabricates itself at a constant rate, given its enzyme kinetics and the stoichiometry of all reactions. For this, we must understand the process of cellular growth: steady-state self-fabrication requires a cell to synthesize all of its components, including metabolites, enzymes and ribosomes, in proportions that match its own composition. Simultaneously, the concentrations of these components affect the rates of metabolism and biosynthesis, and hence the growth rate. We here derive a theory that describes all phenotypes that solve this circular problem. All phenotypes can be described as a combination of minimal building blocks, which we call Elementary Growth Modes (EGMs). EGMs can be used as the theoretical basis for all models that explicitly model self-fabrication, such as the currently popular Metabolism and Expression models. We then use our theory to make concrete biological predictions. We find that natural selection for maximal growth rate drives microorganisms to states of minimal phenotypic complexity: only one EGM will be active when growth rate is maximised. The phenotype of a cell is only extended with one more EGM whenever growth becomes limited by an additional biophysical constraint, such as a limited solvent capacity of a cellular compartment. The theory presented here extends recent results on Elementary Flux Modes: the minimal building blocks of cellular growth models that lack the self-fabrication aspect. Our theory starts from basic biochemical and evolutionary considerations, and describes unicellular life, both in growth-promoting and in stress-inducing environments, in terms of EGMs.
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Affiliation(s)
- Daan H. de Groot
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines & Systems, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Josephus Hulshof
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines & Systems, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank J. Bruggeman
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines & Systems, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Robert Planqué
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines & Systems, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis. Proc Natl Acad Sci U S A 2019; 117:595-601. [PMID: 31871173 PMCID: PMC6955288 DOI: 10.1073/pnas.1910849117] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
To which extent gene regulatory programs are optimized by evolution is one of the fundamental biological questions. Swimming motility is one of the costliest bacterial behaviors, but expression of motility genes nevertheless increases when bacteria grow slower on poor carbon sources. Here, we show that competitive fitness benefit provided by the ability of motile bacteria to follow gradients of secondary nutrients shows similar negative dependence on the growth rate as the investment in motility. Thus, bacteria appear to pre-invest into the motile behavior in proportion to the anticipated benefit that can be provided by chemotaxis when nutrient gradients become available in their environment. Microorganisms possess diverse mechanisms to regulate investment into individual cellular processes according to their environment. How these regulatory strategies reflect the inherent trade-off between the benefit and cost of resource investment remains largely unknown, particularly for many cellular functions that are not immediately related to growth. Here, we investigate regulation of motility and chemotaxis, one of the most complex and costly bacterial behaviors, as a function of bacterial growth rate. We show with experiment and theory that in poor nutritional conditions, Escherichia coli increases its investment in motility in proportion to the reproductive fitness advantage provided by the ability to follow nutrient gradients. Since this growth-rate dependent regulation of motility genes occurs even when nutrient gradients are absent, we hypothesize that it reflects an anticipatory preallocation of cellular resources. Notably, relative fitness benefit of chemotaxis could be observed not only in the presence of imposed gradients of secondary nutrients but also in initially homogeneous bacterial cultures, suggesting that bacteria can generate local gradients of carbon sources and excreted metabolites, and subsequently use chemotaxis to enhance the utilization of these compounds. This interplay between metabolite excretion and their chemotaxis-dependent reutilization is likely to play an important general role in microbial communities.
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The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat Commun 2019; 10:5536. [PMID: 31797920 PMCID: PMC6892915 DOI: 10.1038/s41467-019-13483-w] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 11/08/2019] [Indexed: 12/26/2022] Open
Abstract
Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome. Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.
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Kim J, Darlington A, Salvador M, Utrilla J, Jiménez JI. Trade-offs between gene expression, growth and phenotypic diversity in microbial populations. Curr Opin Biotechnol 2019; 62:29-37. [PMID: 31580950 PMCID: PMC7208540 DOI: 10.1016/j.copbio.2019.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/15/2019] [Accepted: 08/20/2019] [Indexed: 12/13/2022]
Abstract
Limitations in molecular resources for gene expression influence bacterial physiology. Bacteria optimise trade-offs between resource allocation and growth. Resource allocation plays a role in the emergence of phenotypic heterogeneity. Trade-offs between bet-hedging and growth can be harnessed in biotechnology.
Bacterial cells have a limited number of resources that can be allocated for gene expression. The intracellular competition for these resources has an impact on the cell physiology. Bacteria have evolved mechanisms to optimize resource allocation in a variety of scenarios, showing a trade-off between the resources used to maximise growth (e.g. ribosome synthesis) and the rest of cellular functions. Limitations in gene expression also play a role in generating phenotypic diversity, which is advantageous in fluctuating environments, at the expenses of decreasing growth rates. Our current understanding of these trade-offs can be exploited for biotechnological applications benefiting from the selective manipulation of the allocation of resources.
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Affiliation(s)
- Juhyun Kim
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | | | - Manuel Salvador
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | - José Utrilla
- Centre for Genomic Sciences, Universidad Nacional Autónoma de México, Campus Morelos, Av. Universidad s/n Col. Chamilpa 62210, Cuernavaca, Mexico
| | - José I Jiménez
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom.
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45
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Tools and systems for evolutionary engineering of biomolecules and microorganisms. ACTA ACUST UNITED AC 2019; 46:1313-1326. [DOI: 10.1007/s10295-019-02191-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/20/2019] [Indexed: 12/28/2022]
Abstract
Abstract
Evolutionary approaches have been providing solutions to various bioengineering challenges in an efficient manner. In addition to traditional adaptive laboratory evolution and directed evolution, recent advances in synthetic biology and fluidic systems have opened a new era of evolutionary engineering. Synthetic genetic circuits have been created to control mutagenesis and enable screening of various phenotypes, particularly metabolite production. Fluidic systems can be used for high-throughput screening and multiplexed continuous cultivation of microorganisms. Moreover, continuous directed evolution has been achieved by combining all the steps of evolutionary engineering. Overall, modern tools and systems for evolutionary engineering can be used to establish the artificial equivalent to natural evolution for various research applications.
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Abstract
Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden;
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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Mori M, Marinari E, De Martino A. A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth. NPJ Syst Biol Appl 2019; 5:16. [PMID: 31069113 PMCID: PMC6494807 DOI: 10.1038/s41540-019-0093-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 04/12/2019] [Indexed: 12/21/2022] Open
Abstract
Living cells react to changes in growth conditions by re-shaping their proteome. This accounts for different stress-response strategies, both specific (i.e., aimed at increasing the availability of stress-mitigating proteins) and systemic (such as large-scale changes in the use of metabolic pathways aimed at a more efficient exploitation of resources). Proteome re-allocation can, however, imply significant biosynthetic costs. Whether and how such costs impact the growth performance are largely open problems. Focusing on carbon-limited E. coli growth, we integrate genome-scale modeling and proteomic data to address these questions at quantitative level. After deriving a simple formula linking growth rate, carbon intake, and biosynthetic costs, we show that optimal growth results from the tradeoff between yield maximization and protein burden minimization. Empirical data confirm that E. coli growth is indeed close to Pareto-optimal over a broad range of growth rates. Moreover, we establish that, while most of the intaken carbon is diverted into biomass precursors, the efficiency of ATP synthesis is the key driver of the yield-cost tradeoff. These findings provide a quantitative perspective on carbon overflow, the origin of growth laws and the multidimensional optimality of E. coli metabolism.
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Affiliation(s)
- Matteo Mori
- Department of Physics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Enzo Marinari
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 2, Rome, 00185 Italy
- INFN, Sezione di Roma 1, Piazzale Aldo Moro 2, Rome, 00185 Italy
| | - Andrea De Martino
- Soft & Living Matter Lab, Institute of Nanotechnology (CNR-NANOTEC), c/o Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 2, Rome, 00185 Italy
- Italian Institute for Genomic Medicine, via Nizza 52, Turin, 10126 Italy
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de Groot DH, van Boxtel C, Planqué R, Bruggeman FJ, Teusink B. The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates. PLoS Comput Biol 2019; 15:e1006858. [PMID: 30856167 PMCID: PMC6428345 DOI: 10.1371/journal.pcbi.1006858] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 03/21/2019] [Accepted: 02/07/2019] [Indexed: 11/18/2022] Open
Abstract
Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized-in a static environment under mass-conservation and enzyme expression constraints-we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.
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Affiliation(s)
- Daan H. de Groot
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Coco van Boxtel
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Robert Planqué
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank J. Bruggeman
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- * E-mail:
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Chen Y, Vu J, Thompson MG, Sharpless WA, Chan LJG, Gin JW, Keasling JD, Adams PD, Petzold CJ. A rapid methods development workflow for high-throughput quantitative proteomic applications. PLoS One 2019; 14:e0211582. [PMID: 30763335 PMCID: PMC6375547 DOI: 10.1371/journal.pone.0211582] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 01/16/2019] [Indexed: 12/13/2022] Open
Abstract
Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.
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Affiliation(s)
- Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jonathan Vu
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Mitchell G. Thompson
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, United States of America
| | - William A. Sharpless
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Leanne Jade G. Chan
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jennifer W. Gin
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jay D. Keasling
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, United States of America
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, United States of America
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Paul D. Adams
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, United States of America
- Molecular Biophysics and Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Christopher J. Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
- * E-mail:
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50
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Yang L, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO. DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. BMC SYSTEMS BIOLOGY 2019; 13:2. [PMID: 30626386 PMCID: PMC6327497 DOI: 10.1186/s12918-018-0675-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 12/21/2018] [Indexed: 01/09/2023]
Abstract
BACKGROUND Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. RESULTS We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. CONCLUSIONS Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
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Affiliation(s)
- Laurence Yang
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Ali Ebrahim
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Colton J. Lloyd
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Michael A. Saunders
- Department of Management Science and Engineering, Stanford University, 475 Via Ortega, Stanford, 94305 CA USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kongens Lyngby, 2800 Denmark
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