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de Atauri P, Foguet C, Cascante M. Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery. Biosystems 2023; 231:104984. [PMID: 37506820 DOI: 10.1016/j.biosystems.2023.104984] [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: 05/08/2023] [Revised: 07/18/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023]
Abstract
Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.
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Affiliation(s)
- Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of Universitat de Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, 28020, Spain.
| | - Carles Foguet
- British Heart Foundation Cardiovascular Epidemiology Unit and Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, CB2 0BD, United Kingdom
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of Universitat de Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, 28020, Spain.
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2
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Angelani CR, Carabias P, Cruz KM, Delfino JM, de Sautu M, Espelt MV, Ferreira-Gomes MS, Gómez GE, Mangialavori IC, Manzi M, Pignataro MF, Saffioti NA, Salvatierra Fréchou DM, Santos J, Schwarzbaum PJ. A metabolic control analysis approach to introduce the study of systems in biochemistry: the glycolytic pathway in the red blood cell. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2018; 46:502-515. [PMID: 30281891 DOI: 10.1002/bmb.21139] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 06/05/2018] [Indexed: 06/08/2023]
Abstract
Metabolic control analysis (MCA) is a promising approach in biochemistry aimed at understanding processes in a quantitative fashion. Here the contribution of enzymes and transporters to the control of a given pathway flux and metabolite concentrations is determined and expressed quantitatively by means of numerical coefficients. Metabolic flux can be influenced by a wide variety of modulators acting on one or more metabolic steps along the pathway. We describe a laboratory exercise to study metabolic regulation of human erythrocytes (RBCs). Within the framework of MCA, students use these cells to determine the sensitivity of the glycolytic flux to two inhibitors (iodoacetic acid: IA, and iodoacetamide: IAA) known to act on the enzyme glyceraldehyde-3-phosphate-dehydrogenase. Glycolytic flux was estimated by determining the concentration of extracellular lactate, the end product of RBC glycolysis. A low-cost colorimetric assay was implemented, that takes advantage of the straightforward quantification of the absorbance signal from the photographic image of the multi-well plate taken with a standard digital camera. Students estimate flux response coefficients for each inhibitor by fitting an empirical function to the experimental data, followed by analytical derivation of this function. IA and IAA exhibit qualitatively different patterns, which are thoroughly analyzed in terms of the physicochemical properties influencing their action on the target enzyme. IA causes highest glycolytic flux inhibition at lower concentration than IAA. This work illustrates the feasibility of using the MCA approach to study key variables of a simple metabolic system, in the context of an upper level biochemistry course. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):502-515, 2018.
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Affiliation(s)
- Carla R Angelani
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Pablo Carabias
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Karen M Cruz
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - José M Delfino
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Marilina de Sautu
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - María V Espelt
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Mariela S Ferreira-Gomes
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Gabriela E Gómez
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Irene C Mangialavori
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Malena Manzi
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - María F Pignataro
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Nicolás A Saffioti
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Damiana M Salvatierra Fréchou
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Javier Santos
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
| | - Pablo J Schwarzbaum
- Departamento de Química Biológica and Institute of Biochemistry and Biophysics (IQUIFIB, UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD, Buenos Aires, Argentina
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3
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Sauro HM. Control and regulation of pathways via negative feedback. J R Soc Interface 2017; 14:20160848. [PMID: 28202588 PMCID: PMC5332569 DOI: 10.1098/rsif.2016.0848] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 01/19/2017] [Indexed: 12/21/2022] Open
Abstract
The biochemical networks found in living organisms include a huge variety of control mechanisms at multiple levels of organization. While the mechanistic and molecular details of many of these control mechanisms are understood, their exact role in driving cellular behaviour is not. For example, yeast glycolysis has been studied for almost 80 years but it is only recently that we have come to understand the systemic role of the multitude of feedback and feed-forward controls that exist in this pathway. In this article, control theory is discussed as an approach to dissect the control logic of complex pathways. One of the key issues is distinguishing between the terms control and regulation and how these concepts are applied to regulated enzymes such as phosphofructokinase. In doing so, one of the paradoxes in metabolic regulation can be resolved where enzymes such as phosphofructokinase have little control but, nevertheless, possess significant regulatory influence.
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Affiliation(s)
- Herbert M Sauro
- Department of Bioengineering, William H. Foege Building, Box 355061, University of Washington, Seattle, WA 98195-5061, USA
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Acerenza L, Monzon P, Ortega F. A modular modulation method for achieving increases in metabolite production. Biotechnol Prog 2015; 31:656-67. [PMID: 25683235 DOI: 10.1002/btpr.2059] [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: 09/12/2014] [Revised: 12/25/2014] [Indexed: 11/10/2022]
Abstract
Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice.
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Affiliation(s)
- Luis Acerenza
- Systems Biology Laboratory, Faculty of Sciences, Universidad de la República, Iguá 4225, Montevideo, 11400, Uruguay
| | - Pablo Monzon
- School of Engineering, Universidad de la República, Julio Herrera y Reissig 565, Montevideo, 11300, Uruguay
| | - Fernando Ortega
- Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, Manchester, M13 9PT, UK
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Regulatory design in a simple system integrating membrane potential generation and metabolic ATP consumption. Robustness and the role of energy dissipating processes. BIOCHIMICA ET BIOPHYSICA ACTA-BIOENERGETICS 2011; 1807:1634-46. [PMID: 21945502 DOI: 10.1016/j.bbabio.2011.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2011] [Revised: 09/03/2011] [Accepted: 09/08/2011] [Indexed: 01/02/2023]
Abstract
Bacterial physiological responses integrate energy-coupling processes at the membrane level with metabolic energy demand. The regulatory design behind these responses remains largely unexplored. Propionigenium modestum is an adequate organism to study these responses because it presents the simplest scheme known integrating membrane potential generation and metabolic ATP consumption. A hypothetical sodium leak is added to the scheme as the sole regulatory site. Allosteric regulation is assumed to be absent. Information of the rate equations is not available. However, relevant features of the patterns of responses may be obtained using Metabolic Control Analysis (MCA) and Metabolic Control Design (MCD). With these tools, we show that membrane potential disturbances can be compensated by adjusting the leak flux, without significant perturbations of ATP consumption. Perturbations of membrane potential by ATP demand are inevitable and also require compensatory changes in the leak. Numerical simulations were performed with a kinetic model exhibiting the responses for small changes obtained with MCA and MCD. A modest leak (10% of input) was assumed for the reference state. We found that disturbances in membrane potential and ATP consumption, produced by environmental perturbations of the cation concentration, may be reverted to the reference state adjusting the leak. Leak changes can also compensate for undesirable effects on membrane potential produced by changes in nutrient availability or ATP demand, in a wide range of values. The system is highly robust to parameter fluctuations. The regulatory role of energy dissipating processes and the trade-off between energetic efficiency and regulatory capacity are discussed.
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Ortega F, Acerenza L. Modular metabolic control analysis of large responses in branched systems - application to aspartate metabolism. FEBS J 2011; 278:2565-78. [DOI: 10.1111/j.1742-4658.2011.08184.x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
In this work, a novel optimization-based metabolic control analysis (OMCA) method is introduced for reducing data requirement for metabolic control analysis (MCA). It is postulated that using the optimal control approach, the fluxes in a metabolic network are correlated to metabolite concentrations and enzyme activities as a state-feedback control system that is optimal with respect to a homeostasis objective. It is then shown that the optimal feedback gains are directly related to the elasticity coefficients (ECs) of MCA. This approach requires determination of the relative "importance" of metabolites and fluxes for the system, which is possible with significantly reduced experimental data, as compared with typical MCA requirements. The OMCA approach is applied to a top-down control model of glycolysis in hepatocytes. It is statistically demonstrated that the OMCA model is capable of predicting the ECs observed experimentally with few exceptions. Further, an OMCA-based model reconciliation study shows that the modification of four assumed stoichiometric coefficients in the model can explain most of the discrepancies, with the exception of elasticities with respect to the NADH/NAD ratio.
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Affiliation(s)
- Korkut Uygun
- Dept. of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI 48202, USA
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8
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Chen ML, Wang FS. Dynamic sensitivity analysis of oscillating biochemical systems using modified collocation method. J Taiwan Inst Chem Eng 2009. [DOI: 10.1016/j.jtice.2009.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Rodríguez-Prados JC, de Atauri P, Maury J, Ortega F, Portais JC, Chassagnole C, Acerenza L, Lindley ND, Cascante M. In silico strategy to rationally engineer metabolite production: A case study for threonine in Escherichia coli. Biotechnol Bioeng 2009; 103:609-20. [PMID: 19219914 DOI: 10.1002/bit.22271] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Genetic engineering of metabolic pathways is a standard strategy to increase the production of metabolites of economic interest. However, such flux increases could very likely lead to undesirable changes in metabolite concentrations, producing deleterious perturbations on other cellular processes. These negative effects could be avoided by implementing a balanced increase of enzyme concentrations according to the Universal Method [Kacser and Acerenza (1993) Eur J Biochem 216:361-367]. Exact application of the method usually requires modification of many reactions, which is difficult to achieve in practice. Here, improvement of threonine production via pyruvate kinase deletion in Escherichia coli is used as a case study to demonstrate a partial application of the Universal Method, which includes performing sensitivity analysis. Our analysis predicts that manipulating a few reactions is sufficient to obtain an important increase in threonine production without major perturbations of metabolite concentrations.
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Boyle PM, Silver PA. Harnessing nature's toolbox: regulatory elements for synthetic biology. J R Soc Interface 2009; 6 Suppl 4:S535-46. [PMID: 19324675 DOI: 10.1098/rsif.2008.0521.focus] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Synthetic biologists seek to engineer complex biological systems composed of modular elements. Achieving higher complexity in engineered biological organisms will require manipulating numerous systems of biological regulation: transcription; RNA interactions; protein signalling; and metabolic fluxes, among others. Exploiting the natural modularity at each level of biological regulation will promote the development of standardized tools for designing biological systems.
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Affiliation(s)
- Patrick M Boyle
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Abstract
We study the effect of extrinsic noise in metabolic networks. We introduce external random fluctuations at the kinetic level, and show how these lead to a stochastic generalization of standard metabolic control analysis. While summation and connectivity theorems hold true in the presence of extrinsic noise, control coefficients incorporate its effect through an explicit dependency on the noise intensity. New elasticities and response coefficients are also defined. Accordingly, the concept of control by noise is introduced as a way of tuning the systemic behaviour of metabolisms. We argue that this framework holds for intrinsic noise too, when time-scale separation is present in the system.
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Affiliation(s)
- Andrea Rocco
- Department of Mathematical Sciences, University of Bath, Bath, UK
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Ortega F, Cascante M, Acerenza L. Kinetic properties required for sustained or paradoxical control of metabolic fluxes under large changes in enzyme activities. J Theor Biol 2007; 252:569-73. [PMID: 18045618 DOI: 10.1016/j.jtbi.2007.10.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2007] [Accepted: 10/18/2007] [Indexed: 10/22/2022]
Abstract
The effect that an increase in the activity of an enzyme has on its flux normally decreases with activity increase. To achieve a large increase in flux by manipulating a single step would therefore require a high initial effect that maintains or increases when the activity is increased, what has been called sustained or paradoxical control. Using metabolic control analysis for large responses, we derive conditions for sustained or paradoxical control in terms of elasticity coefficients. These are used to characterise types of rate laws contributing to this behaviour. The result that simple pathways, with normal kinetics, subject to large activity changes can lead to paradoxical control behaviour suggests that this type of pattern may be much more ubiquitous than could have, in principle, been suspected.
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Affiliation(s)
- Fernando Ortega
- Department of Biochemistry and Molecular Biology, Associated Unit to CSIC, Spain
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