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Sato G, Kinoshita S, Yamada TG, Arai S, Kitaguchi T, Funahashi A, Doi N, Fujiwara K. Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution. ACS Synth Biol 2024; 13:1572-1581. [PMID: 38717981 DOI: 10.1021/acssynbio.4c00209] [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: 05/18/2024]
Abstract
Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.
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Affiliation(s)
- Gaku Sato
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Saki Kinoshita
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Takahiro G Yamada
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
- Department of Molecular Biology, University of California San Diego, La Jolla, California 92093, United States
| | - Satoshi Arai
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Tetsuya Kitaguchi
- Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho, Yokohama, Kanagawa 226-8503, Japan
| | - Akira Funahashi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Nobuhide Doi
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Kei Fujiwara
- Department of Biosciences & Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
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Ghosh K. Stoichiometric versus stochastic interaction in models of liquid-liquid phase separation. Biophys J 2022; 121:4-6. [PMID: 34932937 PMCID: PMC8758413 DOI: 10.1016/j.bpj.2021.12.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/01/2021] [Accepted: 12/06/2021] [Indexed: 01/07/2023] Open
Affiliation(s)
- Kingshuk Ghosh
- Department of Physics and Astronomy, University of Denver, Denver,Molecular and Cellular Biophysics, University of Denver, Denver,Corresponding author
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Chen BR, You CX, Shu CC. The common misuse of noise decomposition as applied to genetic systems. Biosystems 2020; 198:104269. [PMID: 33038463 DOI: 10.1016/j.biosystems.2020.104269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/22/2020] [Accepted: 10/02/2020] [Indexed: 10/23/2022]
Abstract
The noise-decomposition technique is applied in several fields, including genetic systems, optical images, recording, and navigation. In genetic systems, noise decomposition is usually achieved by using two reporters [Elowitz M.B., Levine A.J., Siggia E.D., Swain P·S., 2002. Stochastic gene expression in a single cell. Science 297, 1183-6.]. A reporter is a protein with fluorescence, an RNA hybridized with a fluorescent probe, or any other detectable intracellular component. If a reporter is constructed in addition to the original reporter, the system's stochasticity may change. Such phenomena became severe for genes in plasmids with a high copy number. By SSA (stochastic simulation algorithm), we observed an approximately 50% increment in the coefficient of variation while introducing additional reporters. Besides, if two reporters respond to the upstream element at a different time, the trunk noise (or extrinsic noise) cannot be accurately determined. This is because the "calculative trunk noise" changes along with the delay, though the real trunk noise does not. For RNA reporters, a 5-min transcriptional delay caused a calculative trunk noise that was 90% less than the real trunk noise. Fortunately, this problem is negligible when the degradation rate constant is low, and it is usually true in the case of the protein reporters. One can check the lifespan of the reporter before applying the noise-decomposition technique.
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Affiliation(s)
- Bo-Ren Chen
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Chao-Xuan You
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Che-Chi Shu
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan.
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Weistuch C, Pressé S. Spatiotemporal Organization of Catalysts Driven by Enhanced Diffusion. J Phys Chem B 2017; 122:5286-5290. [DOI: 10.1021/acs.jpcb.7b06868] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- C. Weistuch
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - S. Pressé
- Department of Physics, IUPUI Indianapolis, Indianapolis, Indiana 46202, United States
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Inhibitors Alter the Stochasticity of Regulatory Proteins to Force Cells to Switch to the Other State in the Bistable System. Sci Rep 2017; 7:4413. [PMID: 28667253 PMCID: PMC5493615 DOI: 10.1038/s41598-017-04596-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 05/17/2017] [Indexed: 12/19/2022] Open
Abstract
The cellular behaviors under the control of genetic circuits are subject to stochastic fluctuations, or noise. The stochasticity in gene regulation, far from a nuisance, has been gradually appreciated for its unusual function in cellular activities. In this work, with Chemical Master Equation (CME), we discovered that the addition of inhibitors altered the stochasticity of regulatory proteins. For a bistable system of a mutually inhibitory network, such a change of noise led to the migration of cells in the bimodal distribution. We proposed that the consumption of regulatory protein caused by the addition of inhibitor is not the only reason for pushing cells to the specific state; the change of the intracellular stochasticity is also the main cause for the redistribution. For the level of the inhibitor capable of driving 99% of cells, if there is no consumption of regulatory protein, 88% of cells were guided to the specific state. It implied that cells were pushed, by the inhibitor, to the specific state due to the change of stochasticity.
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Das D, Dey S, Brewster RC, Choubey S. Effect of transcription factor resource sharing on gene expression noise. PLoS Comput Biol 2017; 13:e1005491. [PMID: 28414750 PMCID: PMC5411101 DOI: 10.1371/journal.pcbi.1005491] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 05/01/2017] [Accepted: 03/31/2017] [Indexed: 12/31/2022] Open
Abstract
Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it’s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression. Genetically identical cells, even when they are exposed to the same environmental conditions, display incredible diversity. Gene expression noise is attributed to be a key source of this phenotypic diversity. Transcriptional dynamics is a dominant source of expression noise. Although scores of theoretical and experimental studies have explored how noise is regulated at the level of transcription, most of them focus on the gene specific, cis regulatory elements, such as the number of transcription factor (TF) binding sites, their binding strength, etc. However, how the global properties of transcription, such as the limited availability of TFs impact noise in gene expression remains rather elusive. Here we build a theoretical model that incorporates the effect of limiting TF pool on gene expression noise. We find that competition between genes for TFs leads to enhanced variability in mRNA copy number across an isogenic population. Moreover, for gene copies sharing TFs with other competitor sites, mRNA variance as a function of the mean shows distinct imprints for one gene copy and multiple gene copies respectively. This stands in sharp contrast to the universal behavior found in mean expression irrespective of the different scenarios of competition. An interesting feature of competition is that introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution, which could lead to phenotypic variability.
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Affiliation(s)
- Dipjyoti Das
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
| | - Supravat Dey
- Laboratoire Charles Coulomb, Université de Montpellier and CNRS, Montpellier, France
| | - Robert C. Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
| | - Sandeep Choubey
- FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
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Gruebele M, Thirumalai D. Perspective: Reaches of chemical physics in biology. J Chem Phys 2013; 139:121701. [PMID: 24089712 PMCID: PMC5942441 DOI: 10.1063/1.4820139] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2013] [Accepted: 08/20/2013] [Indexed: 01/09/2023] Open
Abstract
Chemical physics as a discipline contributes many experimental tools, algorithms, and fundamental theoretical models that can be applied to biological problems. This is especially true now as the molecular level and the systems level descriptions begin to connect, and multi-scale approaches are being developed to solve cutting edge problems in biology. In some cases, the concepts and tools got their start in non-biological fields, and migrated over, such as the idea of glassy landscapes, fluorescence spectroscopy, or master equation approaches. In other cases, the tools were specifically developed with biological physics applications in mind, such as modeling of single molecule trajectories or super-resolution laser techniques. In this introduction to the special topic section on chemical physics of biological systems, we consider a wide range of contributions, all the way from the molecular level, to molecular assemblies, chemical physics of the cell, and finally systems-level approaches, based on the contributions to this special issue. Chemical physicists can look forward to an exciting future where computational tools, analytical models, and new instrumentation will push the boundaries of biological inquiry.
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Affiliation(s)
- Martin Gruebele
- Departments of Chemistry and Physics, and Center for Biophysics and Computational Biology, University of Illinois, Urbana, Illinois 61801, USA
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