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Alves LDF, Moore JB, Kell DB. The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects. Int J Mol Sci 2024; 25:9082. [PMID: 39201768 PMCID: PMC11354673 DOI: 10.3390/ijms25169082] [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] [Received: 07/19/2024] [Revised: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/03/2024] Open
Abstract
Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.
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Affiliation(s)
- Luana de Fátima Alves
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Søltofts Plads, 2800 Kongens Lyngby, Denmark
| | - J. Bernadette Moore
- School of Food Science & Nutrition, University of Leeds, Leeds LS2 9JT, UK;
- Department of Biochemistry, Cell & Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
| | - Douglas B. Kell
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Søltofts Plads, 2800 Kongens Lyngby, Denmark
- Department of Biochemistry, Cell & Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
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2
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Theron CW, Salcedo-Sora JE, Grixti JM, Møller-Hansen I, Borodina I, Kell DB. Evidence for the Role of the Mitochondrial ABC Transporter MDL1 in the Uptake of Clozapine and Related Molecules into the Yeast Saccharomyces cerevisiae. Pharmaceuticals (Basel) 2024; 17:938. [PMID: 39065789 PMCID: PMC11279418 DOI: 10.3390/ph17070938] [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: 03/21/2024] [Revised: 05/25/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Clozapine is an antipsychotic drug whose accumulation in white cells can sometimes prove toxic; understanding the transporters and alleles responsible is thus highly desirable. We used a strategy in which a yeast (Saccharomyces cerevisiae) CRISPR-Cas9 knock-out library was exposed to cytotoxic concentrations of clozapine to determine those transporters whose absence made it more resistant; we also recognised the structural similarity of the fluorescent dye safranin O (also known as safranin T) to clozapine, allowing it to be used as a surrogate marker. Strains lacking the mitochondrial ABC transporter MDL1 (encoded by YLR188W) showed substantial resistance to clozapine. MDL1 overexpression also conferred extra sensitivity to clozapine and admitted a massive increase in the cellular and mitochondrial uptake of safranin O, as determined using flow cytometry and microscopically. Yeast lacking mitochondria showed no such unusual accumulation. Mitochondrial MDL1 is thus the main means of accumulation of clozapine in S. cerevisiae. The closest human homologue of S. cerevisiae MDL1 is ABCB10.
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Affiliation(s)
- Chrispian W. Theron
- GeneMill Biofoundry, Liverpool Shared Research Facilities, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK;
| | - J. Enrique Salcedo-Sora
- GeneMill Biofoundry, Liverpool Shared Research Facilities, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK;
| | - Justine M. Grixti
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrated Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Iben Møller-Hansen
- The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Søltofts Plads 220, 2800 Kongens Lyngby, Denmark
| | - Irina Borodina
- The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Søltofts Plads 220, 2800 Kongens Lyngby, Denmark
| | - Douglas B. Kell
- GeneMill Biofoundry, Liverpool Shared Research Facilities, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK;
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrated Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
- The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Søltofts Plads 220, 2800 Kongens Lyngby, Denmark
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3
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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4
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [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] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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5
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Tummler K, Klipp E. Data integration strategies for whole-cell modeling. FEMS Yeast Res 2024; 24:foae011. [PMID: 38544322 PMCID: PMC11042497 DOI: 10.1093/femsyr/foae011] [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: 12/02/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.
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Affiliation(s)
- Katja Tummler
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
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6
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Westerhoff HV. On paradoxes between optimal growth, metabolic control analysis, and flux balance analysis. Biosystems 2023; 233:104998. [PMID: 37591451 DOI: 10.1016/j.biosystems.2023.104998] [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] [Received: 05/01/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023]
Abstract
In Microbiology it is often assumed that growth rate is maximal. This may be taken to suggest that the dependence of the growth rate on every enzyme activity is at the top of an inverse-parabolic function, i.e. that all flux control coefficients should equal zero. This might seem to imply that the sum of these flux control coefficients equals zero. According to the summation law of Metabolic Control Analysis (MCA) the sum of flux control coefficients should equal 1 however. And in Flux Balance Analysis (FBA) catabolism is often limited by a hard bound, causing catabolism to fully control the fluxes, again in apparent contrast with a flux control coefficient of zero. Here we resolve these paradoxes (apparent contradictions) in an analysis that uses the 'Edinburgh pathway', the 'Amsterdam pathway', as well as a generic metabolic network providing the building blocks or Gibbs energy for microbial growth. We review and show that (i) optimization depends on so-called enzyme control coefficients rather than the 'catalytic control coefficients' of MCA's summation law, (ii) when optimization occurs at fixed total protein, the former differ from the latter to the extent that they may all become equal to zero in the optimum state, (iii) in more realistic scenarios of optimization where catalytically inert biomass is compensating or maintenance metabolism is taken into consideration, the optimum enzyme concentrations should not be expected to equal those that maximize the specific growth rate, (iv) optimization may be in terms of yield rather than specific growth rate, which resolves the paradox because the sum of catalytic control coefficients on yield equals 0, (v) FBA effectively maximizes growth yield, and for yield the summation law states 0 rather than 1, thereby removing the paradox, (vi) furthermore, FBA then comes more often to a 'hard optimum' defined by a maximum catabolic flux and a catabolic-enzyme control coefficient of 1. The trade-off between maintenance metabolism and growth is highlighted as worthy of further analysis.
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Affiliation(s)
- Hans V Westerhoff
- Department of Molecular Cell Biology, Vrije Universiteit Amsterdam, A-Life, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, the Netherlands; School of Biological Sciences, Medicine and Health, University of Manchester, Manchester, United Kingdom; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, 7600, South Africa.
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7
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Fleming RMT, Haraldsdottir HS, Minh LH, Vuong PT, Hankemeier T, Thiele I. Cardinality optimization in constraint-based modelling: application to human metabolism. Bioinformatics 2023; 39:btad450. [PMID: 37697651 PMCID: PMC10495685 DOI: 10.1093/bioinformatics/btad450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/12/2023] [Indexed: 09/13/2023] Open
Abstract
MOTIVATION Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution. RESULTS By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction. AVAILABILITY AND IMPLEMENTATION Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.
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Affiliation(s)
- Ronan M T Fleming
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
| | - Hulda S Haraldsdottir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Le Hoai Minh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Phan Tu Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- Mathematical Sciences School, University of Southampton, University Road, Southampton SO17 1BJ, United Kingdom
| | - Thomas Hankemeier
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
| | - Ines Thiele
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
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8
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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9
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Zhang Y, Westerhoff HV. Gear Shifting in Biological Energy Transduction. ENTROPY (BASEL, SWITZERLAND) 2023; 25:993. [PMID: 37509940 PMCID: PMC10378313 DOI: 10.3390/e25070993] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/18/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Confronted with thermodynamically adverse output processes, free-energy transducers may shift to lower gears, thereby reducing output per unit input. This option is well known for inanimate machines such as automobiles, but unappreciated in biology. The present study extends existing non-equilibrium thermodynamic principles to underpin biological gear shifting and identify possible mechanisms. It shows that gear shifting differs from altering the degree of coupling and that living systems may use it to optimize their performance: microbial growth is ultimately powered by the Gibbs energy of catabolism, which is partially transformed into Gibbs energy ('output force') in the ATP that is produced. If this output force is high, the cell may turn to a catabolic pathway with a lower ATP stoichiometry. Notwithstanding the reduced stoichiometry, the ATP synthesis flux may then actually increase as compared to that in a system without gear shift, in which growth might come to a halt. A 'variomatic' gear switching strategy should be optimal, explaining why organisms avail themselves of multiple catabolic pathways, as these enable them to shift gears when the growing gets tough.
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Affiliation(s)
- Yanfei Zhang
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Hans V Westerhoff
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
- Department of Molecular Cell Biology, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch 7600, South Africa
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10
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Zhang Y, van der Zee L, Barberis M. Two-way communication between cell cycle and metabolism in budding yeast: what do we know? Front Microbiol 2023; 14:1187304. [PMID: 37396387 PMCID: PMC10309209 DOI: 10.3389/fmicb.2023.1187304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
Coordination of cell cycle and metabolism exists in all cells. The building of a new cell is a process that requires metabolic commitment to the provision of both Gibbs energy and building blocks for proteins, nucleic acids, and membranes. On the other hand, the cell cycle machinery will assess and regulate its metabolic environment before it makes decisions on when to enter the next cell cycle phase. Furthermore, more and more evidence demonstrate that the metabolism can be regulated by cell cycle progression, as different biosynthesis pathways are preferentially active in different cell cycle phases. Here, we review the available literature providing a critical overview on how cell cycle and metabolism may be coupled with one other, bidirectionally, in the budding yeast Saccharomyces cerevisiae.
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Affiliation(s)
- Yanfei Zhang
- Molecular Systems Biology, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Lucas van der Zee
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Matteo Barberis
- Molecular Systems Biology, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, Surrey, United Kingdom
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11
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Shin J, Porubsky V, Carothers J, Sauro HM. Standards, dissemination, and best practices in systems biology. Curr Opin Biotechnol 2023; 81:102922. [PMID: 37004298 PMCID: PMC10435326 DOI: 10.1016/j.copbio.2023.102922] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/14/2023] [Accepted: 02/24/2023] [Indexed: 04/03/2023]
Abstract
The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
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Affiliation(s)
- Janis Shin
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Veronica Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - James Carothers
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
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12
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Aharoni A, Goodacre R, Fernie AR. Plant and microbial sciences as key drivers in the development of metabolomics research. Proc Natl Acad Sci U S A 2023; 120:e2217383120. [PMID: 36930598 PMCID: PMC10041103 DOI: 10.1073/pnas.2217383120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.
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Affiliation(s)
- Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot76100, Israel
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7BE, UK
| | - Alisdair R. Fernie
- Max-Planck-Institute for Molecular Plant Physiology, Potsdam14476, Germany
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13
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Recent Advances In Microbe-Photocatalyst Hybrid Systems for Production of Bulk Chemicals: A Review. Appl Biochem Biotechnol 2023; 195:1574-1588. [PMID: 36346559 DOI: 10.1007/s12010-022-04169-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2022] [Indexed: 11/11/2022]
Abstract
Solar-driven biocatalysis technologies can combine inorganic photocatalytic materials with biological catalysts to convert CO2, light, and water into chemicals, offering the promise of high energy efficiency and a broader product scope than that of natural photosynthesis. Solar energy is the most abundant renewable energy source on earth, but it cannot be directly utilized by current industrial microorganisms. Therefore, the establishment of a solar-driven bio-catalysis platform, a bridge between solar energy and heterotrophic microorganisms, can dramatically increase carbon flux in biomanufacturing systems and consequently may revolutionize the biorefinery. This review first discusses the main applications of microbe-photocatalyst hybrid (MPH) systems in biorefinery processes. Then, various strategies to improve the electron transfer by microorganisms at the inorganic photocatalytic material interface are discussed, especially biohybrid systems based on autotrophic or heterotrophic bacteria and photocatalytic materials. Finally, we discuss the current challenges and offer potential solutions for the development of MPH systems.
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14
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Parameter Identification in Metabolic Reaction Networks by Means of Multiple Steady-State Measurements. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
In this work, we investigate some theoretical aspects related to the estimation approach proposed by Liebermeister and Klipp, 2006, in which general rate laws, derived from standardized enzymatic mechanisms, are exploited to kinetically describe the fluxes of a metabolic reaction network, and multiple metabolic steady-state measurements are exploited to estimate the unknown kinetic parameters. Further mathematical details are deeply investigated, and necessary conditions on the amount of information required to solve the identification problem are given. Moreover, theoretical results for the parameter identifiability are provided, and symmetrical and modular properties of the proposed approach are highlighted when the global identification problem is decoupled into smaller and simpler identification problems related to the single reactions of the network. Among the advantages of the proposed innovative approach are (i) non-restrictive conditions to guarantee the solvability of the parameter estimation problem, (ii) the unburden of the usual computational complexity for such identification problems, and (iii) the ease of obtaining the required number of measurements, which are actually steady-state data, experimentally easier to obtain with respect to the time-dependent ones. A simple example concludes the paper, highlighting the mentioned advantages of the method and the implementation of the related theoretical result.
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15
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Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions. Metabolites 2023; 13:metabo13010088. [PMID: 36677014 PMCID: PMC9862193 DOI: 10.3390/metabo13010088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/14/2022] [Accepted: 12/23/2022] [Indexed: 01/07/2023] Open
Abstract
Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, 13C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.
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16
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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17
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Jouhten P, Konstantinidis D, Pereira F, Andrejev S, Grkovska K, Castillo S, Ghiachi P, Beltran G, Almaas E, Mas A, Warringer J, Gonzalez R, Morales P, Patil KR. Predictive evolution of metabolic phenotypes using model-designed environments. Mol Syst Biol 2022; 18:e10980. [PMID: 36201279 PMCID: PMC9536503 DOI: 10.15252/msb.202210980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/04/2022] Open
Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
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Affiliation(s)
- Paula Jouhten
- European Molecular Biology LaboratoryHeidelbergGermany
- VTT Technical Research Centre of Finland LtdEspooFinland
- Department of Bioproducts and BiosystemsAalto UniversityEspooFinland
| | | | | | | | | | | | - Payam Ghiachi
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Gemma Beltran
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Albert Mas
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Jonas Warringer
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Ramon Gonzalez
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Pilar Morales
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Kiran R Patil
- European Molecular Biology LaboratoryHeidelbergGermany
- Medical Research Council (MRC) Toxicology UnitUniversity of CambridgeCambridgeUK
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18
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Chaudhuri S, Srivastava A. Network approach to understand biological systems: From single to multilayer networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Meng L, Yoshida N, Li Z. Soil microorganisms facilitated the electrode-driven trichloroethene dechlorination to ethene by Dehalococcoides species in a bioelectrochemical system. ENVIRONMENTAL RESEARCH 2022; 209:112801. [PMID: 35093309 DOI: 10.1016/j.envres.2022.112801] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/17/2021] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
Bioelectrochemical dechlorination using organohalide-respiring bacteria (ORBs) is a promising technique for remediating contaminated groundwater. Generally, a longer enrichment period is required for selecting the ORB consortia to achieve bioelectrochemical dechlorination. However, the full dechloriantion is difficult to be achieved due to the absence of functional species (e.g. Dehalococcoides) in previously used enrich cultures. To overcome these challenges, bioelectrochemical dechlorination using a culture enriched with the pre-augmented Dehalococcoides was performed for the first time in this study. A two-chamber bioelectrochemical system (BES) inoculated with a pure Dehalococcoides culture and paddy soil with an applied voltage of -0.3 V (versus a standard hydrogen electrode) as the sole electron donor was used to achieve dechlorination. The ethene formation rate was 10-100 times higher than that in previous studies, indicating that inoculating the system with a pure Dehalococcoides culture and soil microorganisms gave effective full dechlorination performance. Microbial community analysis and bioelectrochemical analysis indicated that Desulfosporosinus species may have facilitated dechlorination through syntrophic interactions with Dehalococcoides. The results indicated that adding Dehalococcoides cells before operating a bioelectrochemical system is an effective way of achieving full dechlorination.
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Affiliation(s)
- Lingyu Meng
- Department of Civil Engineering, Nagoya Institute of Technology (Nitech), Nagoya, 466-8555, Japan.
| | - Naoko Yoshida
- Department of Civil Engineering, Nagoya Institute of Technology (Nitech), Nagoya, 466-8555, Japan
| | - Zhiling Li
- State Key Laboratory of Urban Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
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20
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Chen Y, Li F, Nielsen J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 2022; 22:foac003. [PMID: 35094064 PMCID: PMC8862083 DOI: 10.1093/femsyr/foac003] [Citation(s) in RCA: 16] [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: 11/29/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 11/30/2022] Open
Abstract
Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Feiran Li
- 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
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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21
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Oliver SG. From Petri Plates to Petri Nets, a revolution in yeast biology. FEMS Yeast Res 2022; 22:6526310. [PMID: 35142857 PMCID: PMC8862034 DOI: 10.1093/femsyr/foac008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 01/26/2022] [Accepted: 02/07/2022] [Indexed: 11/22/2022] Open
Affiliation(s)
- Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK
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22
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Claus S, Jezierska S, Elbourne LDH, Van Bogaert I. Exploring the transportome of the biosurfactant producing yeast Starmerella bombicola. BMC Genomics 2022; 23:22. [PMID: 34998388 PMCID: PMC8742932 DOI: 10.1186/s12864-021-08177-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/16/2021] [Indexed: 12/13/2022] Open
Abstract
Starmerella bombicola is a non-conventional yeast mainly known for its capacity to produce high amounts of the glycolipids 'sophorolipids'. Although its product has been used as biological detergent for a couple of decades, the genetics of S. bombicola are still largely unknown. Computational analysis of the yeast's genome enabled us to identify 254 putative transporter genes that make up the entire transportome. For each of them, a potential substrate was predicted using homology analysis, subcellular localization prediction and RNA sequencing in different stages of growth. One transporter family is of exceptional importance to this yeast: the ATP Binding Cassette (ABC) transporter Superfamily, because it harbors the main driver behind the highly efficient sophorolipid export. Furthermore, members of this superfamily translocate a variety of compounds ranging from antibiotics to hydrophobic molecules. We conducted an analysis of this family by creating deletion mutants to understand their role in the export of hydrophobic compounds, antibiotics and sophorolipids. Doing this, we could experimentally confirm the transporters participating in the efflux of medium chain fatty alcohols, particularly decanol and undecanol, and identify a second sophorolipid transporter that is located outside the sophorolipid biosynthetic gene cluster.
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Affiliation(s)
- Silke Claus
- Centre for Synthetic Biology, Department of Biotechnology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Sylwia Jezierska
- Centre for Synthetic Biology, Department of Biotechnology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Liam D H Elbourne
- Department of Molecular Sciences, Macquarie University, Macquarie Park, NSW, 2109, Australia
| | - Inge Van Bogaert
- Centre for Synthetic Biology, Department of Biotechnology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
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23
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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24
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Caspeta L, Kerkhoven EJ, Martinez A, Nielsen J. The yeastGemMap: A process diagram to assist yeast systems-metabolic studies. Biotechnol Bioeng 2021; 118:4800-4814. [PMID: 34569624 DOI: 10.1002/bit.27943] [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: 06/28/2021] [Revised: 09/10/2021] [Accepted: 09/20/2021] [Indexed: 11/09/2022]
Abstract
Visualization is a key aspect of the analysis of omics data. Although many tools can generate pathway maps for visualization of yeast metabolism, they fail in reconstructing genome-scale metabolic diagrams of compartmentalized metabolism. Here we report on the yeastGemMap, a process diagram of whole yeast metabolism created to assist data analysis in systems-metabolic studies. The map was manually reconstructed with reactions from a compartmentalized genome-scale metabolic model, based on biochemical process diagrams typically found in educational and specialized literature. The yeastGemMap consists of 3815 reactions representing 1150 genes, 2742 metabolites, and 14 compartments. Computational functions for adapting the graphical representation of the map are also reported. These functions modify the visual representation of the map to assist in three systems-metabolic tasks: illustrating reaction networks, interpreting metabolic flux data, and visualizing omics data. The versatility of the yeastGemMap and algorithms to assist visualization of systems-metabolic data was demonstrated in various tasks, including for single lethal reaction evaluation, flux balance analysis, and transcriptomic data analysis. For instance, visual interpretation of metabolic transcriptomes of thermally evolved and parental yeast strains allowed to demonstrate that evolved strains activate a preadaptation response at 30°C, which enabled thermotolerance. A quick interpretation of systems-metabolic data is promoted with yeastGemMap visualizations.
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Affiliation(s)
- Luis Caspeta
- Departament of Cellular Engineering and Biocatalysis, Institute of Biotechnology, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Eduard J Kerkhoven
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Alfredo Martinez
- Departament of Cellular Engineering and Biocatalysis, Institute of Biotechnology, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.,BioInnovation Institute, Copenhagen N, Denmark
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25
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Seif Y, Palsson BØ. Path to improving the life cycle and quality of genome-scale models of metabolism. Cell Syst 2021; 12:842-859. [PMID: 34555324 PMCID: PMC8480436 DOI: 10.1016/j.cels.2021.06.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 02/17/2021] [Accepted: 06/23/2021] [Indexed: 11/28/2022]
Abstract
Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.
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Affiliation(s)
- Yara Seif
- Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Bernhard Ørn Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
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26
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Diederen T, Delabrière A, Othman A, Reid ME, Zamboni N. Metabolomics. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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27
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Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol 2021; 40:291-305. [PMID: 34303549 DOI: 10.1016/j.tibtech.2021.06.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 12/21/2022]
Abstract
The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.
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28
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Shah HA, Liu J, Yang Z, Feng J. Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways. Front Mol Biosci 2021; 8:634141. [PMID: 34222327 PMCID: PMC8247443 DOI: 10.3389/fmolb.2021.634141] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
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Affiliation(s)
- Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Jing Feng
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
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Mohy Eldin A, Kamel Z, Hossam N. Purification and identification of surface active amphiphilic candidates produced by Geotrichum candidum MK880487 possessing antifungal property. J DISPER SCI TECHNOL 2021. [DOI: 10.1080/01932691.2020.1813157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Ahmed Mohy Eldin
- Department of Soil Microbiology, Soils, Waters and Environmental Research Institute, Agricultural Research Center, Giza, Egypt
| | - Zeinat Kamel
- Department of Microbiology, Faculty of Science, Cairo University, Giza, Egypt
| | - Nermeen Hossam
- Department of Soil Microbiology, Soils, Waters and Environmental Research Institute, Agricultural Research Center, Giza, Egypt
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Škrlj B, Novak MP, Brader G, Anžič B, Ramšak Ž, Gruden K, Kralj J, Kladnik A, Lavrač N, Roitsch T, Dermastia M. New Cross-Talks between Pathways Involved in Grapevine Infection with ' Candidatus Phytoplasma solani' Revealed by Temporal Network Modelling. PLANTS (BASEL, SWITZERLAND) 2021; 10:646. [PMID: 33805409 PMCID: PMC8065506 DOI: 10.3390/plants10040646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 12/17/2022]
Abstract
Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with 'Candidatus Phytoplasma solani', and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with 'Ca. P. solani'. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to 'Ca. P. solani' to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.
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Affiliation(s)
- Blaž Škrlj
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Maruša Pompe Novak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
- School of Viticulture and Enology, University of Nova Gorica, 5271 Vipava, Slovenia
| | - Günter Brader
- Austrian Institute of Technology, Bioresources Unit, 3430 Tulln, Austria;
| | - Barbara Anžič
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Živa Ramšak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Kristina Gruden
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Jan Kralj
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Aleš Kladnik
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Nada Lavrač
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, 2630 Taastrup, Denmark;
| | - Marina Dermastia
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
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Domenzain I, Li F, Kerkhoven EJ, Siewers V. Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. FEMS Yeast Res 2021; 21:foab002. [PMID: 33428734 PMCID: PMC7943257 DOI: 10.1093/femsyr/foab002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/08/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
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32
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Cheng Y, Schlosser P, Hertel J, Sekula P, Oefner PJ, Spiekerkoetter U, Mielke J, Freitag DF, Schmidts M, Kronenberg F, Eckardt KU, Thiele I, Li Y, Köttgen A. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat Commun 2021; 12:964. [PMID: 33574263 PMCID: PMC7878905 DOI: 10.1038/s41467-020-20877-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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Affiliation(s)
- Yurong Cheng
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
- University of Greifswald, University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Ute Spiekerkoetter
- Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Miriam Schmidts
- Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
- Division of Microbiology, National University of Ireland, Galway, University Road, Galway, Ireland
- APC Microbiome Ireland, Galway, Ireland
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- CIBSS - Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
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33
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Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels. ISME JOURNAL 2020; 15:1257-1270. [PMID: 33323978 PMCID: PMC8115155 DOI: 10.1038/s41396-020-00848-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 11/06/2020] [Accepted: 11/18/2020] [Indexed: 12/13/2022]
Abstract
Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal–bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans. Genome-scale model reconstruction of C. albicans with 89% growth accuracy. Mutualism and parasitism are the most common predicted C. albicans-gut bacteria interactions. Metagenomic sequencing and in vitro assays reveal modulators of fungal growth. Alistipes putredinis potentially prevents elevated C. albicans levels.
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34
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Sailwal M, Das AJ, Gazara RK, Dasgupta D, Bhaskar T, Hazra S, Ghosh D. Connecting the dots: Advances in modern metabolomics and its application in yeast system. Biotechnol Adv 2020; 44:107616. [DOI: 10.1016/j.biotechadv.2020.107616] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
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Zhang S, Lu X, Hu C, Li Y, Yang H, Yan H, Fan J, Xu G, Abnet CC, Qiao Y. Serum Metabolomics for Biomarker Screening of Esophageal Squamous Cell Carcinoma and Esophageal Squamous Dysplasia Using Gas Chromatography-Mass Spectrometry. ACS OMEGA 2020; 5:26402-26412. [PMID: 33110968 PMCID: PMC7581083 DOI: 10.1021/acsomega.0c02600] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/30/2020] [Indexed: 05/14/2023]
Abstract
BACKGROUND Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies with poor diagnosis. Esophageal squamous dysplasia (ESD) is considered as an immediate precancerous lesion of ESCC. Lack of biomarkers for discriminating ESCC and ESD from healthy subjects limits the early diagnosis and treatment of ESCC. Therefore, a serum metabolomic strategy was conducted to identify and validate potential metabolic markers for the screening of ESCC and ESD subjects. METHODS A total of 74 patients with ESCC, 72 patients with ESD, and 75 normal control (NC) subjects were enrolled in this study. Gas chromatography-mass spectrometry was used to acquire serum metabolic profiles. Pathway analysis was conducted to uncover the fluctuated metabolic pathways during ESCC. Multivariate analyses were used to screen and validate the biomarkers. RESULTS ESCC, ESD, and NC subjects revealed progressively altered metabolic profiles, in which amino acids globally increased, while fatty acids decreased in ESCCs compared with the control groups. Pathway analysis demonstrated the activated biosynthesis of amino acids and inhibited desaturation of saturated fatty acids. The panel constructed with propanoic acid, linoleic acid, glycerol-3-phosphate, and l-glutamine showed the area under the curve (AUC), sensitivity, and specificity of 0.817, 0.75, and 0.74, respectively, in the discrimination of ESCC/ESD patients from NC subjects. The panel constructed by propanoic acid, l-leucine, and hydroxyproline revealed the AUC, sensitivity, and specificity of 0.819, 0.76, and 0.72, respectively, in the discrimination of ESD from NC subjects. The combination of hypoxanthine, 2-ketoisocaproic acid, l-glutamate, and l-aspartate showed the AUC, sensitivity, and specificity of 0.818, 0.83, and 0.74, respectively, in the discrimination of ESCC patients from ESD subjects. CONCLUSIONS Our study revealed the systematic landscape for metabolic alterations in sera of ESD and ESCC patients. The defined metabolite markers showed reasonable performance in the discrimination of ESCC and ESD patients, and may provide helpful reference for clinicians and biologists.
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Affiliation(s)
- Su Zhang
- Department
of Cancer Epidemiology, National Cancer Center/National Clinical Research
Center for Cancer/Cancer Hospital, Chinese
Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, China
| | - Xin Lu
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian
Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
| | - Chunxiu Hu
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian
Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
| | - Yanli Li
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian
Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
| | - Huan Yang
- Department
of Cancer Epidemiology, National Cancer Center/National Clinical Research
Center for Cancer/Cancer Hospital, Chinese
Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, China
| | - Huijiao Yan
- Department
of Cancer Epidemiology, National Cancer Center/National Clinical Research
Center for Cancer/Cancer Hospital, Chinese
Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, China
| | - Jinhu Fan
- Department
of Cancer Epidemiology, National Cancer Center/National Clinical Research
Center for Cancer/Cancer Hospital, Chinese
Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, China
- . Tel: 010-87787423
| | - Guowang Xu
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian
Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
- . Tel/Fax: 0086-422-84379530
| | - Christian C. Abnet
- Division
of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Youlin Qiao
- Department
of Cancer Epidemiology, National Cancer Center/National Clinical Research
Center for Cancer/Cancer Hospital, Chinese
Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, China
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36
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Suthers PF, Dinh HV, Fatma Z, Shen Y, Chan SHJ, Rabinowitz JD, Zhao H, Maranas CD. Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production. Metab Eng Commun 2020; 11:e00148. [PMID: 33134082 PMCID: PMC7586132 DOI: 10.1016/j.mec.2020.e00148] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/08/2020] [Accepted: 10/05/2020] [Indexed: 12/18/2022] Open
Abstract
Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model’s quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism’s production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction. Genome-scale metabolic model iIsor850 describes metabolism of I. orientalis SD108. Customized biomass reaction highlights differences with S. cerevisiae. Chemostat data elucidate growth-associated ATP maintenance. Substrate utilization and CRISPR/Cas9 gene knockout phenotypes validate model. Model pinpoints candidate gene deletions coupling succinic acid production to growth.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Siu Hung Joshua Chan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champagne, Urbana, IL, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
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37
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Sulheim S, Kumelj T, van Dissel D, Salehzadeh-Yazdi A, Du C, van Wezel GP, Nieselt K, Almaas E, Wentzel A, Kerkhoven EJ. Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production. iScience 2020; 23:101525. [PMID: 32942174 PMCID: PMC7501462 DOI: 10.1016/j.isci.2020.101525] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/19/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.
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Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Tjaša Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Dino van Dissel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, Faculty of Computer Science and Electrical Engineering, University of Rostock, 18057 Rostock, Germany
| | - Chao Du
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Gilles P. van Wezel
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Kay Nieselt
- Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, 72070 Tübingen, Germany
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Eduard J. Kerkhoven
- Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
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38
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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc Natl Acad Sci U S A 2020; 117:18869-18879. [PMID: 32675233 DOI: 10.1073/pnas.2002959117] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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Romers J, Thieme S, Münzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl 2020; 6:2. [PMID: 31934349 PMCID: PMC6954118 DOI: 10.1038/s41540-019-0120-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/20/2019] [Indexed: 11/09/2022] Open
Abstract
The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.
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Affiliation(s)
- Jesper Romers
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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Salwan R, Sharma A, Sharma V. Recent Advances in Molecular Approaches for Mining Potential Candidate Genes of Trichoderma for Biofuel. Fungal Biol 2020. [DOI: 10.1007/978-3-030-41870-0_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Vartiainen E, Blomberg P, Ilmén M, Andberg M, Toivari M, Penttilä M. Evaluation of synthetic formaldehyde and methanol assimilation pathways in Yarrowia lipolytica. Fungal Biol Biotechnol 2019; 6:27. [PMID: 31890234 PMCID: PMC6918578 DOI: 10.1186/s40694-019-0090-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 12/03/2019] [Indexed: 11/10/2022] Open
Abstract
Background Crude glycerol coming from biodiesel production is an attractive carbon source for biological production of chemicals. The major impurity in preparations of crude glycerol is methanol, which is toxic for most microbes. Development of microbes, which would not only tolerate the methanol, but also use it as co-substrate, would increase the feasibility of bioprocesses using crude glycerol as substrate. Results To prevent methanol conversion to CO2 via formaldehyde and formate, the formaldehyde dehydrogenase (FLD) gene was identified in and deleted from Yarrowia lipolytica. The deletion strain was able to convert methanol to formaldehyde without expression of heterologous methanol dehydrogenases. Further, it was shown that expression of heterologous formaldehyde assimilating enzymes could complement the deletion of FLD. The expression of either 3-hexulose-6-phosphate synthase (HPS) enzyme of ribulose monosphosphate pathway or dihydroxyacetone synthase (DHAS) enzyme of xylulose monosphosphate pathway restored the formaldehyde tolerance of the formaldehyde sensitive Δfld1 strain. Conclusions In silico, the expression of heterologous formaldehyde assimilation pathways enable Y. lipolytica to use methanol as substrate for growth and metabolite production. In vivo, methanol was shown to be converted to formaldehyde and the enzymes of formaldehyde assimilation were actively expressed in this yeast. However, further development is required to enable Y. lipolytica to efficiently use methanol as co-substrate with glycerol.
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Affiliation(s)
- Eija Vartiainen
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
| | - Peter Blomberg
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
| | - Marja Ilmén
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
| | - Martina Andberg
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
| | - Mervi Toivari
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
| | - Merja Penttilä
- VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, 02044 VTT Espoo, Finland
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Mondeel TDGA, Holland P, Nielsen J, Barberis M. ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast. Nucleic Acids Res 2019; 47:7825-7841. [PMID: 31299083 PMCID: PMC6736057 DOI: 10.1093/nar/gkz603] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 06/23/2019] [Accepted: 07/11/2019] [Indexed: 01/18/2023] Open
Abstract
The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.
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Affiliation(s)
- Thierry D G A Mondeel
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, Surrey, UK.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Petter Holland
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE412 96, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE412 96, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, DK2800 Kgs., Denmark
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, Surrey, UK.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
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Ample Arsenite Bio-Oxidation Activity in Bangladesh Drinking Water Wells: A Bonanza for Bioremediation? Microorganisms 2019; 7:microorganisms7080246. [PMID: 31398879 PMCID: PMC6723331 DOI: 10.3390/microorganisms7080246] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/26/2019] [Accepted: 07/31/2019] [Indexed: 11/17/2022] Open
Abstract
Millions of people worldwide are at risk of arsenic poisoning from their drinking water. In Bangladesh the problem extends to rural drinking water wells, where non-biological solutions are not feasible. In serial enrichment cultures of water from various Bangladesh drinking water wells, we found transfer-persistent arsenite oxidation activity under four conditions (aerobic/anaerobic; heterotrophic/autotrophic). This suggests that biological decontamination may help ameliorate the problem. The enriched microbial communities were phylogenetically at least as diverse as the unenriched communities: they contained a bonanza of 16S rRNA gene sequences. These related to Hydrogenophaga, Acinetobacter, Dechloromonas, Comamonas, and Rhizobium/Agrobacterium species. In addition, the enriched microbiomes contained genes highly similar to the arsenite oxidase (aioA) gene of chemolithoautotrophic (e.g., Paracoccus sp. SY) and heterotrophic arsenite-oxidizing strains. The enriched cultures also contained aioA phylotypes not detected in the previous survey of uncultivated samples from the same wells. Anaerobic enrichments disclosed a wider diversity of arsenite oxidizing aioA phylotypes than did aerobic enrichments. The cultivatable chemolithoautotrophic and heterotrophic arsenite oxidizers are of great interest for future in or ex-situ arsenic bioremediation technologies for the detoxification of drinking water by oxidizing arsenite to arsenate that should then precipitates with iron oxides. The microbial activities required for such a technology seem present, amplifiable, diverse and hence robust.
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van Steijn L, Verbeek FJ, Spaink HP, Merks RM. Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio. Zebrafish 2019; 16:348-362. [PMID: 31216234 PMCID: PMC6822484 DOI: 10.1089/zeb.2018.1712] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.
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Affiliation(s)
| | - Fons J. Verbeek
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
| | - Herman P. Spaink
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
| | - Roeland M.H. Merks
- Mathematical Institute, Leiden University, Leiden, Netherlands
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
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46
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Claus S, Jezierska S, Van Bogaert INA. Protein‐facilitated transport of hydrophobic molecules across the yeast plasma membrane. FEBS Lett 2019; 593:1508-1527. [DOI: 10.1002/1873-3468.13469] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 05/31/2019] [Accepted: 06/03/2019] [Indexed: 12/15/2022]
Affiliation(s)
- Silke Claus
- Biochemical and Microbial Technology Universiteit Gent Belgium
| | | | - Inge N. A. Van Bogaert
- Lab. of Industrial Microbiology and Biocatalysis Faculty of Bioscience Engineering Ghent University Belgium
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Abstract
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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Affiliation(s)
- Changdai Gu
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Won Jun Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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48
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Nielsen J. Yeast Systems Biology: Model Organism and Cell Factory. Biotechnol J 2019; 14:e1800421. [PMID: 30925027 DOI: 10.1002/biot.201800421] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/23/2019] [Indexed: 01/02/2023]
Abstract
For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.
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Affiliation(s)
- Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej 3, DK2200, Copenhagen N, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK2800, Kongens Lyngby, Denmark
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A genome-scale metabolic network reconstruction of extremely halophilic bacterium Salinibacter ruber. PLoS One 2019; 14:e0216336. [PMID: 31071110 PMCID: PMC6508672 DOI: 10.1371/journal.pone.0216336] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 04/18/2019] [Indexed: 11/19/2022] Open
Abstract
A genome-scale metabolic network reconstruction of Salinibacter ruber DSM13855 is presented here. To our knowledge, this is the first metabolic model of an organism in the phylum Rhodothermaeota. This model, which will be called iMB631, was reconstructed based on genomic and biochemical data available on the strain Salinibacter ruber DSM13855. This network consists of 1459 reactions, 1363 metabolites and 631 genes. Model evaluation was performed based on existing biochemical data in the literature and also by performing laboratory experiments. For growth on different carbon sources, we show that iMB631 is able to correctly predict the growth in 91% of cases where growth has been observed experimentally and 83% of conditions in which S. ruber did not grow. The F-score was 93%, demonstrating a generally acceptable performance of the model. Based on the predicted flux distributions, we found that under certain autotrophic condition, a reductive tricarboxylic acid cycle (rTCA) has fluxes in all necessary reactions to support autotrophic growth. To include special metabolites of the bacterium, salinixanthin biosynthesis pathway was modeled based on the pathway proposed recently. For years, main glucose consumption pathway has been under debates in S. ruber. Using flux balance analysis, iMB631 predicts pentose phosphate pathway, rather than glycolysis, as the active glucose consumption method in the S. ruber.
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50
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 2019; 15:e1006971. [PMID: 31009451 PMCID: PMC6497307 DOI: 10.1371/journal.pcbi.1006971] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 03/21/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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