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Pereira T, Vilaprinyo E, Belli G, Herrero E, Salvado B, Sorribas A, Altés G, Alves R. Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress. Cell Rep 2019; 22:2421-2430. [PMID: 29490277 DOI: 10.1016/j.celrep.2018.02.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 01/15/2018] [Accepted: 02/05/2018] [Indexed: 11/18/2022] Open
Abstract
Microorganisms evolved adaptive responses to survive stressful challenges in ever-changing environments. Understanding the relationships between the physiological/metabolic adjustments allowing cellular stress adaptation and gene expression changes being used by organisms to achieve such adjustments may significantly impact our ability to understand and/or guide evolution. Here, we studied those relationships during adaptation to various stress challenges in Saccharomyces cerevisiae, focusing on heat stress responses. We combined dozens of independent experiments measuring whole-genome gene expression changes during stress responses with a simplified kinetic model of central metabolism. We identified alternative quantitative ranges for a set of physiological variables in the model (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to either heat stress or desiccation/rehydration. Our approach is scalable to other adaptive responses and could assist in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes.
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Affiliation(s)
- Tania Pereira
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Ester Vilaprinyo
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Gemma Belli
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Enric Herrero
- Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Baldiri Salvado
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Albert Sorribas
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Gisela Altés
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Rui Alves
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain.
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Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O. The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling. Sci Rep 2018; 8:12504. [PMID: 30131500 PMCID: PMC6104047 DOI: 10.1038/s41598-018-30884-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 08/07/2018] [Indexed: 11/09/2022] Open
Abstract
Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.
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Affiliation(s)
- Thomas Pfau
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Nils Christian
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Shyam K Masakapalli
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Mark G Poolman
- Department Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Cluster of Excellence on Plant Sciences CEPLAS, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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Jurina T, Tušek AJ, Čurlin M. Local sensitivity analysis and metabolic control analysis of the biological part of the BTEX bioremediation model. BIOTECHNOL BIOPROC E 2016. [DOI: 10.1007/s12257-015-0049-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Cakır T, Khatibipour MJ. Metabolic network discovery by top-down and bottom-up approaches and paths for reconciliation. Front Bioeng Biotechnol 2014; 2:62. [PMID: 25520953 PMCID: PMC4253960 DOI: 10.3389/fbioe.2014.00062] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 11/14/2014] [Indexed: 11/13/2022] Open
Abstract
The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.
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Affiliation(s)
- Tunahan Cakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology) , Gebze , Turkey
| | - Mohammad Jafar Khatibipour
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology) , Gebze , Turkey ; Department of Chemical Engineering, Gebze Technical University (formerly known as Gebze Institute of Technology) , Gebze , Turkey
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Bazzani S. Promise and reality in the expanding field of network interaction analysis: metabolic networks. Bioinform Biol Insights 2014; 8:83-91. [PMID: 24812497 PMCID: PMC3999820 DOI: 10.4137/bbi.s12466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 03/02/2014] [Accepted: 03/03/2014] [Indexed: 12/25/2022] Open
Abstract
In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.
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Affiliation(s)
- Susanna Bazzani
- PhD candidate in Biophysics. Former laboratory: Computational Systems Biochemistry Group, Charitè Universitätsmedizin, Berlin, Germany
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Price AH, Norton GJ, Salt DE, Ebenhoeh O, Meharg AA, Meharg C, Islam MR, Sarma RN, Dasgupta T, Ismail AM, McNally KL, Zhang H, Dodd IC, Davies WJ. Alternate wetting and drying irrigation for rice in Bangladesh: Is it sustainable and has plant breeding something to offer? Food Energy Secur 2013. [DOI: 10.1002/fes3.29] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Adam H. Price
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - Gareth J. Norton
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - David E. Salt
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - Oliver Ebenhoeh
- Institute of Complex Systems and Mathematical Biology Department of Physics University of Aberdeen Aberdeen AB24 3UE U.K
| | - Andrew A. Meharg
- Institute for Global Food Security Queen's University Belfast David Keir Building Malone Road Belfast BT9 5BN U.K
| | - Caroline Meharg
- Institute for Global Food Security Queen's University Belfast David Keir Building Malone Road Belfast BT9 5BN U.K
| | - M. Rafiqul Islam
- Department of Soil Science Bangladesh Agricultural University Mymensingh Bangladesh
| | - Ramen N. Sarma
- Department of Plant Breeding and Genetics Assam Agricultural University Jorhat 785013 Assam India
| | - Tapash Dasgupta
- Department of Genetics and Plant Breeding Calcutta University 35 B.C. Road Kolkata 700 019 West Bengal India
| | - Abdelbagi M. Ismail
- International Rice Research Institute (IRRI) DAPO 7777 Metro Manila 1031 The Philippines
| | - Kenneth L. McNally
- International Rice Research Institute (IRRI) DAPO 7777 Metro Manila 1031 The Philippines
| | - Hao Zhang
- Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
| | - Ian C. Dodd
- Centre for Sustainable Agriculture Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
| | - William J. Davies
- Centre for Sustainable Agriculture Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
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Berkhout J, Bruggeman FJ, Teusink B. Optimality principles in the regulation of metabolic networks. Metabolites 2012; 2:529-52. [PMID: 24957646 PMCID: PMC3901211 DOI: 10.3390/metabo2030529] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Revised: 08/15/2012] [Accepted: 08/17/2012] [Indexed: 12/14/2022] Open
Abstract
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
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Affiliation(s)
- Jan Berkhout
- Systems Bioinformatics, AIMMS, VU University, 1081 HV, Amsterdam, The Netherlands.
| | - Frank J Bruggeman
- Systems Bioinformatics, AIMMS, VU University, 1081 HV, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Bioinformatics, AIMMS, VU University, 1081 HV, Amsterdam, The Netherlands
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