1
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Systems biology's role in leveraging microalgal biomass potential: Current status and future perspectives. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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2
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Network Reconstruction and Modelling Made Reproducible with moped. Metabolites 2022; 12:metabo12040275. [PMID: 35448462 PMCID: PMC9032245 DOI: 10.3390/metabo12040275] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/24/2022] [Accepted: 03/15/2022] [Indexed: 11/23/2022] Open
Abstract
Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses.
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3
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Moyer D, Pacheco AR, Bernstein DB, Segrè D. Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure. J Mol Evol 2021; 89:472-483. [PMID: 34230992 PMCID: PMC8318951 DOI: 10.1007/s00239-021-10018-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 06/12/2021] [Indexed: 11/15/2022]
Abstract
Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.
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Affiliation(s)
- Devlin Moyer
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Alan R Pacheco
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - David B Bernstein
- Biological Design Center, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Biological Design Center, Boston University, Boston, MA, 02215, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Department of Physics, Boston University, Boston, MA, 02215, USA.
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4
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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5
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Belcour A, Frioux C, Aite M, Bretaudeau A, Hildebrand F, Siegel A. Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species. eLife 2020; 9:61968. [PMID: 33372654 PMCID: PMC7861615 DOI: 10.7554/elife.61968] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/25/2020] [Indexed: 12/13/2022] Open
Abstract
To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses. All the microbes that live in a specific environment, for example an organ, are collectively called the microbiota. In humans, the microbiota of the gut has been extensively studied by sequencing the DNA of the different microbes to identify them and determine the roles they play in health and disease. The DNA sequences of all the members of the microbiota is called the metagenome. The chemical reactions that the gut microbiota perform to produce energy and make the biomolecules they need to survive are collectively referred to as the metabolism of these microbes. Studying the metabolism of the gut microbiota can help researchers understand the roles of the different microbes. However, the large variety of species in the gut microbiota and gaps in the information about them render these studies difficult, despite technology improving quickly. To tackle this issue, Belcour, Frioux et al developed a new piece of software called Metage2Metabo (M2M) that simulates the metabolism of the gut microbiota and describes the metabolic relationships between the different microbes. Metage2Metabo analyses the roles of the metabolic genes of a large number of microbe species to establish how they complement each other metabolically. Then, it can calculate the minimum number of species needed to perform a metabolic role of interest within that microbiota, and which key species are associated with that role. To test the new software, Belcour, Frioux et al. used Metage2Metabo to analyse genomes from the human gut microbiota and from the cow rumen (one of the cow’s stomachs). They showed that even if the metagenome was incomplete, the software was able to make stable predictions of key species involved in metabolic complementarity. Additionally, they also illustrated how the method can be used to study the gut microbiota of individuals. This work presents a new method for determining the metabolic relationships between species within a microbiota. The software is highly flexible and could be adapted to identify key members within different communities. In the context of the gut microbiota, the predictions of Metage2Metabo could shed lights on the interactions between the host and the microbes and contribute to a better understanding of microbe environments.
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Affiliation(s)
| | - Clémence Frioux
- Univ Rennes, Inria, CNRS, IRISA, Rennes, France.,Inria Bordeaux Sud-Ouest, Talence, France.,Gut Microbes and Heath, Quadram Institute, Norwich, United Kingdom.,Digital Biology, Earlham Institute, Norwich, United Kingdom
| | | | - Anthony Bretaudeau
- Univ Rennes, Inria, CNRS, IRISA, Rennes, France.,Inria, UMR IGEPP, BioInformatics Platform for Agroecosystems Arthropods (BIPAA), Rennes, France.,Inria, IRISA, GenOuest Core Facility, Rennes, France
| | - Falk Hildebrand
- Gut Microbes and Heath, Quadram Institute, Norwich, United Kingdom.,Digital Biology, Earlham Institute, Norwich, United Kingdom
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, Rennes, France
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6
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Hanna EM, Zhang X, Eide M, Fallahi S, Furmanek T, Yadetie F, Zielinski DC, Goksøyr A, Jonassen I. ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species. Front Mol Biosci 2020; 7:591406. [PMID: 33324679 PMCID: PMC7726423 DOI: 10.3389/fmolb.2020.591406] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/22/2020] [Indexed: 12/13/2022] Open
Abstract
The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.
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Affiliation(s)
- Eileen Marie Hanna
- Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Xiaokang Zhang
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Marta Eide
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Shirin Fallahi
- Department of Mathematics, University of Bergen, Bergen, Norway
| | | | - Fekadu Yadetie
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Daniel Craig Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Anders Goksøyr
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Inge Jonassen
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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7
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Systematically gap-filling the genome-scale metabolic model of CHO cells. Biotechnol Lett 2020; 43:73-87. [PMID: 33040240 DOI: 10.1007/s10529-020-03021-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/03/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. RESULTS We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. CONCLUSIONS The present CHO model is an important step towards more complete metabolic models of CHO cells.
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8
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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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9
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Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions. Biochem Soc Trans 2020; 48:901-913. [PMID: 32379295 DOI: 10.1042/bst20190667] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/01/2020] [Accepted: 04/03/2020] [Indexed: 01/24/2023]
Abstract
Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.
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10
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Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling. Metabolites 2019; 9:metabo9020028. [PMID: 30736318 PMCID: PMC6409771 DOI: 10.3390/metabo9020028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 01/24/2019] [Accepted: 01/31/2019] [Indexed: 12/22/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.
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11
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Current challenges facing one-step production of l-ascorbic acid. Biotechnol Adv 2018; 36:1882-1899. [DOI: 10.1016/j.biotechadv.2018.07.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 06/20/2018] [Accepted: 07/17/2018] [Indexed: 12/16/2022]
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12
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Tibocha-Bonilla JD, Zuñiga C, Godoy-Silva RD, Zengler K. Advances in metabolic modeling of oleaginous microalgae. BIOTECHNOLOGY FOR BIOFUELS 2018; 11:241. [PMID: 30202436 PMCID: PMC6124020 DOI: 10.1186/s13068-018-1244-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 08/27/2018] [Indexed: 06/08/2023]
Abstract
Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.
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Affiliation(s)
- Juan D. Tibocha-Bonilla
- Grupo de Investigación en Procesos Químicos y Bioquímicos, Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Av. Carrera 30 No. 45-03, Bogotá, D.C. Colombia
| | - Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760 USA
| | - Rubén D. Godoy-Silva
- Grupo de Investigación en Procesos Químicos y Bioquímicos, Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Av. Carrera 30 No. 45-03, Bogotá, D.C. Colombia
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760 USA
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412 USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0436 USA
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13
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Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O. The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling. Sci Rep 2018; 8:12504. [PMID: 30131500 PMCID: PMC6104047 DOI: 10.1038/s41598-018-30884-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 08/07/2018] [Indexed: 11/09/2022] Open
Abstract
Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.
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Affiliation(s)
- Thomas Pfau
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Nils Christian
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Shyam K Masakapalli
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Mark G Poolman
- Department Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Cluster of Excellence on Plant Sciences CEPLAS, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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14
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Ellens KW, Christian N, Singh C, Satagopam VP, May P, Linster CL. Confronting the catalytic dark matter encoded by sequenced genomes. Nucleic Acids Res 2017; 45:11495-11514. [PMID: 29059321 PMCID: PMC5714238 DOI: 10.1093/nar/gkx937] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 10/03/2017] [Indexed: 01/02/2023] Open
Abstract
The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.
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Affiliation(s)
- Kenneth W Ellens
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Nils Christian
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charandeep Singh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Venkata P Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Carole L Linster
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
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15
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Krumholz EW, Libourel IGL. Thermodynamic Constraints Improve Metabolic Networks. Biophys J 2017; 113:679-689. [PMID: 28793222 DOI: 10.1016/j.bpj.2017.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 05/24/2017] [Accepted: 06/02/2017] [Indexed: 10/19/2022] Open
Abstract
In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation.
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Affiliation(s)
- Elias W Krumholz
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota
| | - Igor G L Libourel
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota; Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota; Genome Craft, Saint Paul, Minnesota.
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16
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Loira N, Mendoza S, Paz Cortés M, Rojas N, Travisany D, Genova AD, Gajardo N, Ehrenfeld N, Maass A. Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production. BMC SYSTEMS BIOLOGY 2017; 11:66. [PMID: 28676050 PMCID: PMC5496344 DOI: 10.1186/s12918-017-0441-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 06/09/2017] [Indexed: 11/10/2022]
Abstract
Background Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0441-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicolás Loira
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile. .,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile.
| | - Sebastian Mendoza
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - María Paz Cortés
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile.,Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Santiago, Chile
| | - Natalia Rojas
- Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Dante Travisany
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Alex Di Genova
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Natalia Gajardo
- Centro de Investigación Austral Biotech, Universidad Santo Tomás, Avenida Ejercito 146, Santiago, Chile
| | - Nicole Ehrenfeld
- Centro de Investigación Austral Biotech, Universidad Santo Tomás, Avenida Ejercito 146, Santiago, Chile
| | - Alejandro Maass
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
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17
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Moejes FW, Matuszynska A, Adhikari K, Bassi R, Cariti F, Cogne G, Dikaios I, Falciatore A, Finazzi G, Flori S, Goldschmidt-Clermont M, Magni S, Maguire J, Le Monnier A, Müller K, Poolman M, Singh D, Spelberg S, Stella GR, Succurro A, Taddei L, Urbain B, Villanova V, Zabke C, Ebenhöh O. A systems-wide understanding of photosynthetic acclimation in algae and higher plants. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:2667-2681. [PMID: 28830099 DOI: 10.1093/jxb/erx137] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 03/28/2017] [Indexed: 05/27/2023]
Abstract
The ability of phototrophs to colonise different environments relies on robust protection against oxidative stress, a critical requirement for the successful evolutionary transition from water to land. Photosynthetic organisms have developed numerous strategies to adapt their photosynthetic apparatus to changing light conditions in order to optimise their photosynthetic yield, which is crucial for life on Earth to exist. Photosynthetic acclimation is an excellent example of the complexity of biological systems, where highly diverse processes, ranging from electron excitation over protein protonation to enzymatic processes coupling ion gradients with biosynthetic activity, interact on drastically different timescales from picoseconds to hours. Efficient functioning of the photosynthetic apparatus and its protection is paramount for efficient downstream processes, including metabolism and growth. Modern experimental techniques can be successfully integrated with theoretical and mathematical models to promote our understanding of underlying mechanisms and principles. This review aims to provide a retrospective analysis of multidisciplinary photosynthetic acclimation research carried out by members of the Marie Curie Initial Training Project, AccliPhot, placing the results in a wider context. The review also highlights the applicability of photosynthetic organisms for industry, particularly with regards to the cultivation of microalgae. It intends to demonstrate how theoretical concepts can successfully complement experimental studies broadening our knowledge of common principles in acclimation processes in photosynthetic organisms, as well as in the field of applied microalgal biotechnology.
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Affiliation(s)
- Fiona Wanjiku Moejes
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
- Bantry Marine Research Station, Gearhies, Bantry, Co. Cork, Ireland P75 AX07
| | - Anna Matuszynska
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
| | - Kailash Adhikari
- Department of Biological and Medical Sciences, Oxford Brookes University, United Kingdom
| | - Roberto Bassi
- University of Verona, Department of Biotechnology, Italy
| | - Federica Cariti
- Department of Botany and Plant Biology, University of Geneva, Switzerland
| | | | | | - Angela Falciatore
- Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de l'Ecole de Médecine, 75006 Paris, France
| | - Giovanni Finazzi
- Laboratoire de Physiologie Cellulaire et Végétale, UMR 5168, Centre National de la Recherche Scientifique (CNRS), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut National Recherche Agronomique (INRA), Institut de Biosciences et Biotechnologie de Grenoble (BIG), Université Grenoble Alpes (UGA), Grenoble 38100, France
| | - Serena Flori
- Laboratoire de Physiologie Cellulaire et Végétale, UMR 5168, Centre National de la Recherche Scientifique (CNRS), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut National Recherche Agronomique (INRA), Institut de Biosciences et Biotechnologie de Grenoble (BIG), Université Grenoble Alpes (UGA), Grenoble 38100, France
| | | | - Stefano Magni
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
| | - Julie Maguire
- Bantry Marine Research Station, Gearhies, Bantry, Co. Cork, Ireland P75 AX07
| | | | - Kathrin Müller
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
| | - Mark Poolman
- Bantry Marine Research Station, Gearhies, Bantry, Co. Cork, Ireland P75 AX07
| | - Dipali Singh
- Bantry Marine Research Station, Gearhies, Bantry, Co. Cork, Ireland P75 AX07
| | - Stephanie Spelberg
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
| | - Giulio Rocco Stella
- Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de l'Ecole de Médecine, 75006 Paris, France
| | - Antonella Succurro
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
| | - Lucilla Taddei
- Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de l'Ecole de Médecine, 75006 Paris, France
| | - Brieuc Urbain
- LUNAM, University of Nantes, GEPEA, UMR-CNRS 6144, France
| | | | | | - Oliver Ebenhöh
- Cluster of Excellence on Plant Sciences (CEPLAS), Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, Germany
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18
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Hosseini Z, Marashi SA. Discovering missing reactions of metabolic networks by using gene co-expression data. Sci Rep 2017; 7:41774. [PMID: 28150713 PMCID: PMC5288723 DOI: 10.1038/srep41774] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 12/30/2016] [Indexed: 11/23/2022] Open
Abstract
Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.
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Affiliation(s)
- Zhaleh Hosseini
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
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19
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Prigent S, Frioux C, Dittami SM, Thiele S, Larhlimi A, Collet G, Gutknecht F, Got J, Eveillard D, Bourdon J, Plewniak F, Tonon T, Siegel A. Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks. PLoS Comput Biol 2017; 13:e1005276. [PMID: 28129330 PMCID: PMC5302834 DOI: 10.1371/journal.pcbi.1005276] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 02/10/2017] [Accepted: 11/30/2016] [Indexed: 11/18/2022] Open
Abstract
Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.
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Affiliation(s)
- Sylvain Prigent
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
- * E-mail: (AS); (SP)
| | - Clémence Frioux
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Simon M. Dittami
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, Roscoff, France
| | | | - Abdelhalim Larhlimi
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Guillaume Collet
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Fabien Gutknecht
- Molecular Genetics, Genomics and Microbiology - GMGM, Université de Strasbourg, Strasbourg, France
| | - Jeanne Got
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Damien Eveillard
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Jérémie Bourdon
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Frédéric Plewniak
- Molecular Genetics, Genomics and Microbiology - GMGM, Université de Strasbourg, Strasbourg, France
- GMGM, CNRS, Strasbourg, France
| | - Thierry Tonon
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, Roscoff, France
| | - Anne Siegel
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
- * E-mail: (AS); (SP)
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20
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Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol 2016; 2:16180. [PMID: 27723761 DOI: 10.1038/nmicrobiol.2016.180] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 08/23/2016] [Indexed: 12/21/2022]
Abstract
The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra- and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.
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21
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Bordron P, Latorre M, Cortés MP, González M, Thiele S, Siegel A, Maass A, Eveillard D. Putative bacterial interactions from metagenomic knowledge with an integrative systems ecology approach. Microbiologyopen 2015; 5:106-17. [PMID: 26677108 PMCID: PMC4767419 DOI: 10.1002/mbo3.315] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 10/12/2015] [Accepted: 10/19/2015] [Indexed: 12/25/2022] Open
Abstract
Following the trend of studies that investigate microbial ecosystems using different metagenomic techniques, we propose a new integrative systems ecology approach that aims to decipher functional roles within a consortium through the integration of genomic and metabolic knowledge at genome scale. For the sake of application, using public genomes of five bacterial strains involved in copper bioleaching: Acidiphilium cryptum, Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans, we first reconstructed a global metabolic network. Next, using a parsimony assumption, we deciphered sets of genes, called Sets from Genome Segments (SGS), that (1) are close on their respective genomes, (2) take an active part in metabolic pathways and (3) whose associated metabolic reactions are also closely connected within metabolic networks. Overall, this SGS paradigm depicts genomic functional units that emphasize respective roles of bacterial strains to catalyze metabolic pathways and environmental processes. Our analysis suggested that only few functional metabolic genes are horizontally transferred within the consortium and that no single bacterial strain can accomplish by itself the whole copper bioleaching. The use of SGS pinpoints a functional compartmentalization among the investigated species and exhibits putative bacterial interactions necessary for promoting these pathways.
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Affiliation(s)
- Philippe Bordron
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Mauricio Latorre
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Santiago, Chile.,Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, Santiago, Chile
| | - Maria-Paz Cortés
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Mauricio González
- Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Santiago, Chile.,Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, Santiago, Chile
| | - Sven Thiele
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Anne Siegel
- IRISA, UMR 6074, CNRS, Rennes, France.,INRIA, Dyliss Team, Centre Rennes-Bretagne-Atlantique, Rennes, France
| | - Alejandro Maass
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Santiago, Chile.,Department of Mathematical Engineering, Universidad de Chile, Santiago, Chile
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22
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Krumholz EW, Libourel IGL. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks. J Biol Chem 2015; 290:19197-207. [PMID: 26041773 DOI: 10.1074/jbc.m114.634121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Indexed: 11/06/2022] Open
Abstract
Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.
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Affiliation(s)
| | - Igor G L Libourel
- From the Department of Plant Biology and the Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota 55108
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23
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Massucci FA, Guimerà R, Nunes Amaral LA, Sales-Pardo M. Scaling and optimal synergy: Two principles determining microbial growth in complex media. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:062703. [PMID: 26172732 PMCID: PMC6653625 DOI: 10.1103/physreve.91.062703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Indexed: 06/04/2023]
Abstract
High-throughput experimental techniques and bioinformatics tools make it possible to obtain reconstructions of the metabolism of microbial species. Combined with mathematical frameworks such as flux balance analysis, which assumes that nutrients are used so as to maximize growth, these reconstructions enable us to predict microbial growth. Although such predictions are generally accurate, these approaches do not give insights on how different nutrients are used to produce growth, and thus are difficult to generalize to new media or to different organisms. Here, we propose a systems-level phenomenological model of metabolism inspired by the virial expansion. Our model predicts biomass production given the nutrient uptakes and a reduced set of parameters, which can be easily determined experimentally. To validate our model, we test it against in silico simulations and experimental measurements of growth, and find good agreement. From a biological point of view, our model uncovers the impact that individual nutrients and the synergistic interaction between nutrient pairs have on growth, and suggests that we can understand the growth maximization principle as the optimization of nutrient synergies.
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Affiliation(s)
| | - Roger Guimerà
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
| | - Luís A. Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
- Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, Illinois 60208, USA
- Howard Hughes Medical Institute, Northwestern University, Evanston, Illinois 60208, USA
| | - Marta Sales-Pardo
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
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24
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Scaife MA, Nguyen GTDT, Rico J, Lambert D, Helliwell KE, Smith AG. Establishing Chlamydomonas reinhardtii as an industrial biotechnology host. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2015; 82:532-546. [PMID: 25641561 PMCID: PMC4515103 DOI: 10.1111/tpj.12781] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 01/19/2015] [Accepted: 01/20/2015] [Indexed: 05/20/2023]
Abstract
Microalgae constitute a diverse group of eukaryotic unicellular organisms that are of interest for pure and applied research. Owing to their natural synthesis of value-added natural products microalgae are emerging as a source of sustainable chemical compounds, proteins and metabolites, including but not limited to those that could replace compounds currently made from fossil fuels. For the model microalga, Chlamydomonas reinhardtii, this has prompted a period of rapid development so that this organism is poised for exploitation as an industrial biotechnology platform. The question now is how best to achieve this? Highly advanced industrial biotechnology systems using bacteria and yeasts were established in a classical metabolic engineering manner over several decades. However, the advent of advanced molecular tools and the rise of synthetic biology provide an opportunity to expedite the development of C. reinhardtii as an industrial biotechnology platform, avoiding the process of incremental improvement. In this review we describe the current status of genetic manipulation of C. reinhardtii for metabolic engineering. We then introduce several concepts that underpin synthetic biology, and show how generic parts are identified and used in a standard manner to achieve predictable outputs. Based on this we suggest that the development of C. reinhardtii as an industrial biotechnology platform can be achieved more efficiently through adoption of a synthetic biology approach.
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Affiliation(s)
- Mark A Scaife
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
- *For correspondence (e-mails or )
| | - Ginnie TDT Nguyen
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
| | - Juan Rico
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
| | - Devinn Lambert
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
| | - Katherine E Helliwell
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
| | - Alison G Smith
- Department of Plant Science, University of CambridgeDowning Street, Cambridge, CB2 3EA, UK
- *For correspondence (e-mails or )
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25
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Reijnders MJ, van Heck RG, Lam CM, Scaife MA, Santos VAMD, Smith AG, Schaap PJ. Green genes: bioinformatics and systems-biology innovations drive algal biotechnology. Trends Biotechnol 2014; 32:617-26. [DOI: 10.1016/j.tibtech.2014.10.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Revised: 09/30/2014] [Accepted: 10/01/2014] [Indexed: 01/18/2023]
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Prigent S, Collet G, Dittami SM, Delage L, Ethis de Corny F, Dameron O, Eveillard D, Thiele S, Cambefort J, Boyen C, Siegel A, Tonon T. The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2014; 80:367-81. [PMID: 25065645 DOI: 10.1111/tpj.12627] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 07/04/2014] [Accepted: 07/21/2014] [Indexed: 06/03/2023]
Abstract
Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.
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Affiliation(s)
- Sylvain Prigent
- Université de Rennes 1, IRISA UMR 6074, Campus de Beaulieu, 35042, Rennes, France; CNRS, IRISA UMR 6074, Campus de Beaulieu, 35042, Rennes, France; Centre Rennes-Bretagne-Atlantique, Projet Dyliss, INRIA, Campus de Beaulieu, 35042, Rennes Cedex, France
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Polle JEW, Neofotis P, Huang A, Chang W, Sury K, Wiech EM. Carbon partitioning in green algae (chlorophyta) and the enolase enzyme. Metabolites 2014; 4:612-28. [PMID: 25093929 PMCID: PMC4192683 DOI: 10.3390/metabo4030612] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 07/25/2014] [Accepted: 07/28/2014] [Indexed: 02/08/2023] Open
Abstract
The exact mechanisms underlying the distribution of fixed carbon within photoautotrophic cells, also referred to as carbon partitioning, and the subcellular localization of many enzymes involved in carbon metabolism are still unknown. In contrast to the majority of investigated green algae, higher plants have multiple isoforms of the glycolytic enolase enzyme, which are differentially regulated in higher plants. Here we report on the number of gene copies coding for the enolase in several genomes of species spanning the major classes of green algae. Our genomic analysis of several green algae revealed the presence of only one gene coding for a glycolytic enolase [EC 4.2.1.11]. Our predicted cytosolic localization would require export of organic carbon from the plastid to provide substrate for the enolase and subsequent re-import of organic carbon back into the plastids. Further, our comparative sequence study of the enolase and its 3D-structure prediction may suggest that the N-terminal extension found in green algal enolases could be involved in regulation of the enolase activity. In summary, we propose that the enolase represents one of the crucial regulatory bottlenecks in carbon partitioning in green algae.
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Affiliation(s)
- Jürgen E W Polle
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
| | - Peter Neofotis
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
| | - Andy Huang
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
| | - William Chang
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
| | - Kiran Sury
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
| | - Eliza M Wiech
- Department of Biology, Brooklyn College of the City University of New York, 2900 Bedford Avenue 200NE, Brooklyn, NY 11210, USA.
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Dittami SM, Eveillard D, Tonon T. A metabolic approach to study algal-bacterial interactions in changing environments. Mol Ecol 2014; 23:1656-60. [PMID: 24447216 DOI: 10.1111/mec.12670] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 01/10/2014] [Accepted: 01/10/2014] [Indexed: 12/16/2022]
Abstract
Increasing evidence exists that bacterial communities interact with and shape the biology of algae and that their evolutionary histories are connected. Despite these findings, physiological studies were and still are generally carried out with axenic or at least antibiotic-treated cultures. Here, we argue that considering interactions between algae and associated bacteria is key to understanding their biology and evolution. To deal with the complexity of the resulting 'holobiont' system, a metabolism-centred approach that uses combined metabolic models for algae and associated bacteria is proposed. We believe that these models will be valuable tools both to study algal-bacterial interactions and to elucidate processes important for the acclimation of the holobiont to environmental changes.
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Affiliation(s)
- Simon M Dittami
- Sorbonne Universités, UPMC Univ Paris 06, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, Roscoff Cedex, F-29688, France; CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, Roscoff Cedex, F-29688, France
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29
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Corfe BM, Evans CA. Are proteins a redundant ontology? Epistemological limitations in the analysis of multistate species. MOLECULAR BIOSYSTEMS 2014; 10:1228-35. [PMID: 24531646 DOI: 10.1039/c3mb70558g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Advances in proteomics have exponentially increased the numbers of post-translational modifications identified, the resulting volume of data is overwhelming both databases and empiricists. We review methodologies for chemical and functional PTM assignment. Using β-oxidation as a paradigm, we discuss epistemic limitations and conceptual approaches to resolving them combining relational biology, proteomics, and the erosion of "protein" and "metabolite" as distinct ontologies.
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Affiliation(s)
- Bernard M Corfe
- Molecular Gastroenterology Research Group, Academic Unit of Surgical Oncology, Department of Oncology, University of Sheffield, UK.
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Vlassis N, Pacheco MP, Sauter T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 2014; 10:e1003424. [PMID: 24453953 PMCID: PMC3894152 DOI: 10.1371/journal.pcbi.1003424] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 11/20/2013] [Indexed: 12/14/2022] Open
Abstract
Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.
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Affiliation(s)
- Nikos Vlassis
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg City, Luxembourg
| | - Maria Pires Pacheco
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
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31
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Use of a global metabolic network to curate organismal metabolic networks. Sci Rep 2013; 3:1695. [PMID: 23603845 PMCID: PMC3631772 DOI: 10.1038/srep01695] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Accepted: 04/04/2013] [Indexed: 12/18/2022] Open
Abstract
The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks.
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32
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Dreyfuss JM, Zucker JD, Hood HM, Ocasio LR, Sachs MS, Galagan JE. Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus Neurospora crassa using FARM. PLoS Comput Biol 2013; 9:e1003126. [PMID: 23935467 PMCID: PMC3730674 DOI: 10.1371/journal.pcbi.1003126] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Accepted: 05/20/2013] [Indexed: 11/18/2022] Open
Abstract
The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms. Few organisms have been as foundational to the development of modern genetics and cellular metabolism as Neurospora crassa. Given the wealth of knowledge available for this filamentous fungus, the effort required to manually curate a high-quality genome-scale metabolic reconstruction would be daunting. To aid the reconstruction process, we developed three optimization-based algorithms. The first algorithm predicts flux while linearly accounting for metabolite dilution; the second algorithm removes blocked reactions with one compact linear program; and the third algorithm reconciles differences between in silico predictions and experimental observations of mutant viability. We have used these algorithms to develop the first genome-scale metabolic model for Neurospora. We have validated the accuracy of our model against an independent test set of more than 300 growth/no-growth phenotypes, and our model displays 93% sensitivity and specificity. Simulating the biochemical genetics experiments originally performed on Neurospora, we comprehensively predicted essential genes, nutrient rescues of auxotroph mutants and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. The model, the algorithms and the testable hypotheses provide a computational foundation for the study of Neurospora crassa metabolism.
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Affiliation(s)
- Jonathan M. Dreyfuss
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
| | - Jeremy D. Zucker
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Tardigrade Biotechnologies, Jamaica Plain, Massachusetts, United States of America
| | - Heather M. Hood
- Institute of Environmental Health, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Linda R. Ocasio
- Tardigrade Biotechnologies, Jamaica Plain, Massachusetts, United States of America
| | - Matthew S. Sachs
- Department of Biology, Texas A&M University, College Station, Texas, United States of America
| | - James E. Galagan
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- * E-mail:
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Hildebrand M, Abbriano RM, Polle JEW, Traller JC, Trentacoste EM, Smith SR, Davis AK. Metabolic and cellular organization in evolutionarily diverse microalgae as related to biofuels production. Curr Opin Chem Biol 2013; 17:506-14. [PMID: 23538202 DOI: 10.1016/j.cbpa.2013.02.027] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2012] [Revised: 02/18/2013] [Accepted: 02/26/2013] [Indexed: 12/18/2022]
Abstract
Microalgae are among the most diverse organisms on the planet, and as a result of symbioses and evolutionary selection, the configuration of core metabolic networks is highly varied across distinct algal classes. The differences in photosynthesis, carbon fixation and processing, carbon storage, and the compartmentation of cellular and metabolic processes are substantial and likely to transcend into the efficiency of various steps involved in biofuel molecule production. By highlighting these differences, we hope to provide a framework for comparative analyses to determine the efficiency of the different arrangements or processes. This sets the stage for optimization on the based on information derived from evolutionary selection to diverse algal classes and to synthetic systems.
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Affiliation(s)
- Mark Hildebrand
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0202, USA.
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34
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Hyduke DR, Lewis NE, Palsson BØ. Analysis of omics data with genome-scale models of metabolism. MOLECULAR BIOSYSTEMS 2013; 9:167-74. [PMID: 23247105 PMCID: PMC3594511 DOI: 10.1039/c2mb25453k] [Citation(s) in RCA: 126] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Over the past decade a massive amount of research has been dedicated to generating omics data to gain insight into a variety of biological phenomena, including cancer, obesity, biofuel production, and infection. Although most of these omics data are available publicly, there is a growing concern that much of these data sit in databases without being used or fully analyzed. Statistical inference methods have been widely applied to gain insight into which genes may influence the activities of others in a given omics data set, however, they do not provide information on the underlying mechanisms or whether the interactions are direct or distal. Biochemically, genetically, and genomically consistent knowledge bases are increasingly being used to extract deeper biological knowledge and understanding from these data sets than possible by inferential methods. This improvement is largely due to knowledge bases providing a validated biological context for interpreting the data.
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Affiliation(s)
- Daniel R Hyduke
- Department of Bioengineering, University of California - San Diego, La Jolla, CA 92093-0412, USA.
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Duan G, Christian N, Schwachtje J, Walther D, Ebenhöh O. The Metabolic Interplay between Plants and Phytopathogens. Metabolites 2013; 3:1-23. [PMID: 24957887 PMCID: PMC3901261 DOI: 10.3390/metabo3010001] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 12/18/2012] [Accepted: 12/31/2012] [Indexed: 12/18/2022] Open
Abstract
Plant diseases caused by pathogenic bacteria or fungi cause major economic damage every year and destroy crop yields that could feed millions of people. Only by a thorough understanding of the interaction between plants and phytopathogens can we hope to develop strategies to avoid or treat the outbreak of large-scale crop pests. Here, we studied the interaction of plant-pathogen pairs at the metabolic level. We selected five plant-pathogen pairs, for which both genomes were fully sequenced, and constructed the corresponding genome-scale metabolic networks. We present theoretical investigations of the metabolic interactions and quantify the positive and negative effects a network has on the other when combined into a single plant-pathogen pair network. Merged networks were examined for both the native plant-pathogen pairs as well as all other combinations. Our calculations indicate that the presence of the parasite metabolic networks reduce the ability of the plants to synthesize key biomass precursors. While the producibility of some precursors is reduced in all investigated pairs, others are only impaired in specific plant-pathogen pairs. Interestingly, we found that the specific effects on the host's metabolism are largely dictated by the pathogen and not by the host plant. We provide graphical network maps for the native plant-pathogen pairs to allow for an interactive interrogation. By exemplifying a systematic reconstruction of metabolic network pairs for five pathogen-host pairs and by outlining various theoretical approaches to study the interaction of plants and phytopathogens on a biochemical level, we demonstrate the potential of investigating pathogen-host interactions from the perspective of interacting metabolic networks that will contribute to furthering our understanding of mechanisms underlying a successful invasion and subsequent establishment of a parasite into a plant host.
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Affiliation(s)
- Guangyou Duan
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.
| | - Nils Christian
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen City AB24 3UE, Aberdeen, United Kingdom.
| | - Jens Schwachtje
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.
| | - Dirk Walther
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.
| | - Oliver Ebenhöh
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen City AB24 3UE, Aberdeen, United Kingdom.
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Vitkin E, Shlomi T. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. Genome Biol 2012. [PMID: 23194418 PMCID: PMC4053740 DOI: 10.1186/gb-2012-13-11-r111] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genome-scale metabolic network reconstructions are considered a key step in
quantifying the genotype-phenotype relationship. We present a novel gap-filling
approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies
missing network reactions by integrating metabolic flux analysis and functional
genomics data. MIRAGE's performance is demonstrated on the reconstruction of
metabolic network models of E. coli and Synechocystis sp. and
validated via existing networks for these species. Then, it is applied to reconstruct
genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for
constraint-based modeling analysis and specifically for metabolic engineering. The
reconstructed network models are supplied via standard SBML files.
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37
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Mastrobuoni G, Irgang S, Pietzke M, Assmus HE, Wenzel M, Schulze WX, Kempa S. Proteome dynamics and early salt stress response of the photosynthetic organism Chlamydomonas reinhardtii. BMC Genomics 2012; 13:215. [PMID: 22651860 PMCID: PMC3444938 DOI: 10.1186/1471-2164-13-215] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2011] [Accepted: 05/31/2012] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The cellular proteome and metabolome are underlying dynamic regulation allowing rapid adaptation to changes in the environment. System-wide analysis of these dynamics will provide novel insights into mechanisms of stress adaptation for higher photosynthetic organisms. We applied pulsed-SILAC labeling to a photosynthetic organism for the first time and we established a method to study proteome dynamics in the green alga Chlamydomonas reinhardtii, an emerging model system for plant biology. In addition, we combined the analysis of protein synthesis with metabolic profiling to study the dynamic changes of metabolism and proteome turnover under salt stress conditions. RESULTS To study de novo protein synthesis an arginine auxotroph Chlamydomonas strain was cultivated in presence of stable isotope-labeled arginine for 24 hours. From the time course experiment in 3 salt concentrations we could identify more than 2500 proteins and their H/L ratio in at least one experimental condition; for 998 protiens at least 3 ratio counts were detected in the 24 h time point (0 mM NaCl). After fractionation we could identify 3115 proteins and for 1765 of them we determined their de novo synthesis rate. Consistently with previous findings we showed that RuBisCO is among the most prominent proteins in the cell; and similar abundance and turnover for the small and large RuBisCO subunit could be calculated. The D1 protein was identified among proteins with a high synthesis rates. A global median half-life of 45 h was calculated for Chlamydomonas proteins under the chosen conditions. CONCLUSION To investigate the temporal co-regulation of the proteome and metabolome, we applied salt stress to Chlamydomonas and studied the time dependent regulation of protein expression and changes in the metabolome. The main metabolic response to salt stress was observed within the amino acid metabolism. In particular, proline was up-regulated manifold and according to that an increased carbon flow within the proline biosynthetic pathway could be measured. In parallel the analysis of abundance and de novo synthesis of the corresponding enzymes revealed that metabolic rearrangements precede adjustments of protein abundance.
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Affiliation(s)
- Guido Mastrobuoni
- Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
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Orth JD, Palsson B. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions. BMC SYSTEMS BIOLOGY 2012; 6:30. [PMID: 22548736 PMCID: PMC3423039 DOI: 10.1186/1752-0509-6-30] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 05/01/2012] [Indexed: 11/13/2022]
Abstract
Background The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network. Results A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. Conclusions Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.
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Affiliation(s)
- Jeffrey D Orth
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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Krumholz EW, Yang H, Weisenhorn P, Henry CS, Libourel IGL. Genome-wide metabolic network reconstruction of the picoalga Ostreococcus. JOURNAL OF EXPERIMENTAL BOTANY 2012; 63:2353-2362. [PMID: 22207618 DOI: 10.1093/jxb/err407] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The green picoalga Ostreococcus is emerging as a simple plant model organism, and two species, O. lucimarinus and O. tauri, have now been sequenced and annotated manually. To evaluate the completeness of the metabolic annotation of both species, metabolic networks of O. lucimarinus and O. tauri were reconstructed from the KEGG database, thermodynamically constrained, elementally balanced, and functionally evaluated. The draft networks contained extensive gaps and, in the case of O. tauri, no biomass components could be produced due to an incomplete Calvin cycle. To find and remove gaps from the networks, an extensive reference biochemical reaction database was assembled using a stepwise approach that minimized the inclusion of microbial reactions. Gaps were then removed from both Ostreococcus networks using two existing gap-filling methodologies. In the first method, a bottom-up approach, a minimal list of reactions was added to each model to enable the production of all metabolites included in our biomass equation. In the second method, a top-down approach, all reactions in the reference database were added to the target networks and subsequently trimmed away based on the sequence alignment scores of identified orthologues. Because current gap-filling methods do not produce unique solutions, a quality metric that includes a weighting for phylogenetic distance and sequence similarity was developed to distinguish between gap-filling results automatically. The draft O. lucimarinus and O. tauri networks required the addition of 56 and 70 reactions, respectively, in order to produce the same biomass precursor metabolites that were produced by our plant reference database.
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Affiliation(s)
- Elias W Krumholz
- Department of Plant Biology, University of Minnesota, Saint Paul, MN 55108, USA
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40
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Handorf T, Klipp E. Modeling mechanistic biological networks: an advanced Boolean approach. ACTA ACUST UNITED AC 2011; 28:557-63. [PMID: 22199382 DOI: 10.1093/bioinformatics/btr697] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The understanding of the molecular sources for diseases like cancer can be significantly improved by computational models. Recently, Boolean networks have become very popular for modeling signaling and regulatory networks. However, such models rely on a set of Boolean functions that are in general not known. Unfortunately, while detailed information on the molecular interactions becomes available in large scale through electronic databases, the information on the Boolean functions does not become available simultaneously and has to be included manually into the models, if at all known. RESULTS We propose a new Boolean approach which can directly utilize the mechanistic network information available through modern databases. The Boolean function is implicitly defined by the reaction mechanisms. Special care has been taken for the treatment of kinetic features like inhibition. The method has been applied to a signaling model combining the Wnt and MAPK pathway. AVAILABILITY A sample C++ implementation of the proposed method is available for Linux and compatible systems through http://code.google.com/p/libscopes/wiki/Paper2011.
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Affiliation(s)
- T Handorf
- Department of Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany.
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Winck FV, Riaño-Pachón DM, Sommer F, Rupprecht J, Mueller-Roeber B. The nuclear proteome of the green alga Chlamydomonas reinhardtii. Proteomics 2011; 12:95-100. [PMID: 22065562 DOI: 10.1002/pmic.201000782] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2010] [Revised: 08/23/2011] [Accepted: 10/11/2011] [Indexed: 02/03/2023]
Abstract
Nuclear proteins play a central role in regulating gene expression. Their identification is important for understanding how the nuclear repertoire changes over time under different conditions. Nuclear proteins are often underrepresented in proteomic studies due to the frequently low abundance of proteins involved in regulatory processes. So far, only few studies describing the nuclear proteome of plant species have been published. Recently, the genome sequence of the unicellular green alga Chlamydomonas reinhardtii has been obtained and annotated, allowing the development of further detailed studies for this organism. However, a detailed description of its nuclear proteome has not been reported so far. Here, we present an analysis of the nuclear proteome of the sequenced Chlamydomonas strain cc503. Using LC-MS/MS, we identified 672 proteins from nuclei isolates with a maximum 1% peptide spectrum false discovery rate. Besides well-known proteins (e.g. histones), transcription factors and other transcriptional regulators (e.g. tubby and HMG) were identified. The presence of protein motifs in nuclear proteins was investigated by computational tools, and specific over-represented protein motifs were identified. This study provides new insights into the complexity of the nuclear environment and reveals novel putative protein targets for further studies of nuclear mechanisms.
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Affiliation(s)
- Flavia V Winck
- GoFORSYS Research Unit for Systems Biology, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
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Tonon T, Eveillard D, Prigent S, Bourdon J, Potin P, Boyen C, Siegel A. Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2011; 15:883-92. [PMID: 22136637 DOI: 10.1089/omi.2011.0089] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach.
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Affiliation(s)
- Thierry Tonon
- UPMC Univ Paris 6 , UMR 7139 Marine Plants and Biomolecules, Station Biologique, 29680 Roscoff, France.
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Pfau T, Christian N, Ebenhöh O. Systems approaches to modelling pathways and networks. Brief Funct Genomics 2011; 10:266-79. [PMID: 21903724 DOI: 10.1093/bfgp/elr022] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
It has become commonly accepted that systems approaches to biology are of outstanding importance to gain understanding from the vast amount of data which is presently being generated by advancing high-throughput technologies. The diversity of methods to model pathways and networks has significantly expanded over the past two decades. Modern and traditional approaches are equally important and recent activities aim at integrating the advantages of both. While traditional methods, based on differential equations, are useful to study the dynamics of small systems, modern constraint-based models can be applied to genome-scale systems, but are not able to capture dynamic features. Integrating different approaches is important to develop consistent theoretical descriptions encompassing various scales of biological information. The rapid progress of the field of theoretical systems biology, however, demonstrates how our fundamental theoretical understanding of biology is gaining momentum. The scientific community has apparently accepted the challenge to truly understand the principles of life.
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Affiliation(s)
- Thomas Pfau
- Department of Physics, University of Aberdeen, Meston Building, Meston Walk, Aberdeen, UK.
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Faust K, Croes D, van Helden J. Prediction of metabolic pathways from genome-scale metabolic networks. Biosystems 2011; 105:109-21. [PMID: 21645586 DOI: 10.1016/j.biosystems.2011.05.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2010] [Revised: 03/23/2011] [Accepted: 05/05/2011] [Indexed: 01/06/2023]
Abstract
The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with variations or novel pathways. During the last decade, we developed and refined an approach that predicts metabolic pathways from a global metabolic network encompassing all known reactions and their substrates/products, by extracting a subgraph connecting at best a set of seed nodes (compounds, reactions, enzymes or enzyme-coding genes). In this review, we summarize this work, while discussing the problems and pitfalls but also the advantages and applications of network-based metabolic pathway prediction.
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Affiliation(s)
- Karoline Faust
- Research Group of Bioinformatics and (Eco-)Systems Biology (BSB), VIB - Vrije Universiteit Brussel, Pleinlaan, Belgium
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Ecosystems biology of microbial metabolism. Curr Opin Biotechnol 2011; 22:541-6. [PMID: 21592777 DOI: 10.1016/j.copbio.2011.04.018] [Citation(s) in RCA: 89] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2011] [Revised: 03/31/2011] [Accepted: 04/20/2011] [Indexed: 11/22/2022]
Abstract
The metabolic capabilities of many environmentally and medically important microbes can be quantitatively explored using systems biology approaches to metabolic networks. Yet, as we learn more about the complex microbe-microbe and microbe-environment interactions in microbial communities, it is important to understand whether and how system-level approaches can be extended to the ecosystem level. Here we summarize recent work that addresses these challenges at multiple scales, starting from two-species natural and synthetic ecology models, up to biosphere-level approaches. Among the many fascinating open challenges in this field is whether the integration of high throughput sequencing methods and mathematical models will help us capture emerging principles of ecosystem-level metabolic organization and evolution.
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May P, Christian N, Ebenhöh O, Weckwerth W, Walther D. Integration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks. Methods Mol Biol 2011; 694:341-363. [PMID: 21082444 DOI: 10.1007/978-1-60761-977-2_21] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The integrated analysis of different omics-level data sets is most naturally performed in the context of common process or pathway association. In this chapter, the two basic approaches for a metabolic pathway-centric integration of proteomics and metabolomics data are described: the knowledge-based approach relying on existing metabolic pathway information, and a data-driven approach that aims to deduce functional (pathway) associations directly from the data. Relevant algorithmic approaches for the generation of metabolic networks of model organisms, their functional analysis, database resources, visualization and analysis tools will be described. The use of proteomics data in the process of metabolic network reconstruction will be discussed.
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Affiliation(s)
- Patrick May
- Max-Planck-Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
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Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 2010; 6:401. [PMID: 20823844 PMCID: PMC2964116 DOI: 10.1038/msb.2010.56] [Citation(s) in RCA: 272] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2010] [Accepted: 06/25/2010] [Indexed: 12/18/2022] Open
Abstract
The first computational approach for the rapid generation of genome-scale tissue-specific models from a generic species model. A genome scale model of human liver metabolism, which is comprehensively tested and validated using cross-validation and the ability to carry out complex hepatic metabolic functions. The model's flux predictions are shown to correlate with flux measurements across a variety of hormonal and dietary conditions, and are successfully used to predict biomarker changes in genetic metabolic disorders, both with higher accuracy than the generic human model.
The study of normal human metabolism and its alterations is central to the understanding and treatment of a variety of human diseases, including diabetes, metabolic syndrome, neurodegenerative disorders, and cancer. A promising systems biology approach for studying human metabolism is through the development and analysis of large-scale stoichiometric network models of human metabolism. The reconstruction of these network models has followed two main paths: the former being the reconstruction of generic (non-tissue specific) models, characterizing the complete metabolic potential of human cells, based mostly on genomic data to trace enzyme-coding genes (Duarte et al, 2007; Ma et al, 2007), and the latter is the reconstruction of cell type- and tissue-specific models (Wiback and Palsson, 2002; Chatziioannou et al, 2003; Vo et al, 2004), based on a similar methodology to that described above, with the extra complexity of manual curation of literature evidence for the cell/system specificity of metabolic enzymes and pathways. On this background, we present in this study, to the best of our knowledge, the first computational approach for a rapid generation of genome-scale tissue-specific models. The method relies on integrating the previously reconstructed generic human models with a variety of high-throughput molecular ‘omics' data, including transcriptomic, proteomic, metabolomic, and phenotypic data, as well as literature-based knowledge, characterizing the tissue in hand (Figure 1). Hence, it can be readily used to quite rapidly build and use a large array of human tissue-specific models. The resulting model satisfies stoichiometric, mass-balance, and thermodynamic constraints. It serves as a functional metabolic network that can then be used to explore the metabolic state of a tissue under various genetic and physiological conditions, simulating enzymatic inhibition or drug applications through standard constraint-based modeling methods, without requiring additional context-specific molecular data. We applied this approach to build a genome scale model of liver metabolism, which is then comprehensively tested and validated. The model is shown to be able to simulate complex hepatic metabolic functions, as well as depicting the pathological alterations caused by urea cycle deficiencies. The liver model was applied to predict measured intra-cellular metabolic fluxes given measured metabolite uptake and secretion rates at different hepatic metabolic conditions. The predictions were tested using a comprehensive set of flux measurements performed by (Chan et al, 2003), showing that the liver model obtained more accurate predictions compared to those obtained by the original, generic human model (an overall prediction accuracy of 0.67 versus 0.46). Furthermore, it was applied to identify metabolic biomarkers for liver in-born errors of metabolism—once again, displaying superiority vs. the predictions generated by the generic human model (accuracy of 0.67 versus 0.59). From a biotechnological standpoint, the liver model generated here can serve as a basis for future studies aiming to optimize the functioning of bio artificial liver devices. The application of the method to rapidly construct metabolic models of other human tissues can obviously lead to many other important clinical insights, e.g., concerning means for metabolic salvage of ischemic heart and brain tissues. Last but not least, the application of the new method is not limited to the realm of human modeling; it can be used to generate tissue models for any multi-tissue organism for which a generic model exists, such as the Mus musculus (Quek and Nielsen, 2008; Sheikh et al, 2005) and the model plant Arabidopsis thaliana (Poolman et al, 2009). The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.
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Affiliation(s)
- Livnat Jerby
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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Schütte M, Skupin A, Segrè D, Ebenhöh O. Modeling the complex dynamics of enzyme-pathway coevolution. CHAOS (WOODBURY, N.Y.) 2010; 20:045115. [PMID: 21198127 DOI: 10.1063/1.3530440] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Metabolic pathways must have coevolved with the corresponding enzyme gene sequences. However, the evolutionary dynamics ensuing from the interplay between metabolic networks and genomes is still poorly understood. Here, we present a computational model that generates putative evolutionary walks on the metabolic network using a parallel evolution of metabolic reactions and their catalyzing enzymes. Starting from an initial set of compounds and enzymes, we expand the metabolic network iteratively by adding new enzymes with a probability that depends on their sequence-based similarity to already present enzymes. Thus, we obtain simulated time courses of chemical evolution in which we can monitor the appearance of new metabolites, enzyme sequences, or even entire organisms. We observe that new enzymes do not appear gradually but rather in clusters which correspond to enzyme classes. A comparison with Brownian motion dynamics indicates that our system displays biased random walks similar to diffusion on the metabolic network with long-range correlations. This suggests that a quantitative molecular principle may underlie the appearance of punctuated equilibrium dynamics, whereby enzymes occur in bursts rather than by phyletic gradualism. Moreover, the simulated time courses lead to a putative time-order of enzyme and organism appearance. Among the patterns we detect in these evolutionary trends is a significant correlation between the time of appearance and their enzyme repertoire size. Hence, our approach to metabolic evolution may help understand the rise in complexity at the biochemical and genomic levels.
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Affiliation(s)
- Moritz Schütte
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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Zhang LY, Chang SH, Wang J. How to make a minimal genome for synthetic minimal cell. Protein Cell 2010; 1:427-34. [PMID: 21203957 PMCID: PMC4875141 DOI: 10.1007/s13238-010-0064-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2010] [Accepted: 05/12/2010] [Indexed: 01/19/2023] Open
Abstract
As a key focus of synthetic biology, building a minimal artificial cell has given rise to many discussions. A synthetic minimal cell will provide an appropriate chassis to integrate functional synthetic parts, devices and systems with functions that cannot generally be found in nature. The design and construction of a functional minimal genome is a key step while building such a cell/chassis since all the cell functions can be traced back to the genome. Kinds of approaches, based on bioinformatics and molecular biology, have been developed and proceeded to derive essential genes and minimal gene sets for the synthetic minimal genome. Experiments about streamlining genomes of model bacteria revealed genome reduction led to unanticipated beneficial properties, such as high electroporation efficiency and accurate propagation of recombinant genes and plasmids that were unstable in other strains. Recent achievements in chemical synthesis technology for large DNA segments together with the rapid development of the whole-genome sequencing, have transferred synthesis of genes to assembly of the whole genomes based on oligonucleotides, and thus created strong preconditions for synthesis of artificial minimal genome. Here in this article, we review briefly the history and current state of research in this field and summarize the main methods for making a minimal genome. We also discuss the impacts of minimized genome on metabolism and regulation of artificial cell.
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Affiliation(s)
- Liu-yan Zhang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China
| | - Su-hua Chang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China
| | - Jing Wang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China
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