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Burgunter-Delamare B, Shetty P, Vuong T, Mittag M. Exchange or Eliminate: The Secrets of Algal-Bacterial Relationships. PLANTS (BASEL, SWITZERLAND) 2024; 13:829. [PMID: 38592793 PMCID: PMC10974524 DOI: 10.3390/plants13060829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024]
Abstract
Algae and bacteria have co-occurred and coevolved in common habitats for hundreds of millions of years, fostering specific associations and interactions such as mutualism or antagonism. These interactions are shaped through exchanges of primary and secondary metabolites provided by one of the partners. Metabolites, such as N-sources or vitamins, can be beneficial to the partner and they may be assimilated through chemotaxis towards the partner producing these metabolites. Other metabolites, especially many natural products synthesized by bacteria, can act as toxins and damage or kill the partner. For instance, the green microalga Chlamydomonas reinhardtii establishes a mutualistic partnership with a Methylobacterium, in stark contrast to its antagonistic relationship with the toxin producing Pseudomonas protegens. In other cases, as with a coccolithophore haptophyte alga and a Phaeobacter bacterium, the same alga and bacterium can even be subject to both processes, depending on the secreted bacterial and algal metabolites. Some bacteria also influence algal morphology by producing specific metabolites and micronutrients, as is observed in some macroalgae. This review focuses on algal-bacterial interactions with micro- and macroalgal models from marine, freshwater, and terrestrial environments and summarizes the advances in the field. It also highlights the effects of temperature on these interactions as it is presently known.
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Affiliation(s)
- Bertille Burgunter-Delamare
- Matthias Schleiden Institute of Genetics, Bioinformatics and Molecular Botany, Friedrich Schiller University Jena, 07743 Jena, Germany; (P.S.); (T.V.)
| | - Prateek Shetty
- Matthias Schleiden Institute of Genetics, Bioinformatics and Molecular Botany, Friedrich Schiller University Jena, 07743 Jena, Germany; (P.S.); (T.V.)
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Trang Vuong
- Matthias Schleiden Institute of Genetics, Bioinformatics and Molecular Botany, Friedrich Schiller University Jena, 07743 Jena, Germany; (P.S.); (T.V.)
| | - Maria Mittag
- Matthias Schleiden Institute of Genetics, Bioinformatics and Molecular Botany, Friedrich Schiller University Jena, 07743 Jena, Germany; (P.S.); (T.V.)
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany
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Cruz F, Capela J, Ferreira EC, Rocha M, Dias O. BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:215-226. [PMID: 38170658 DOI: 10.1109/tcbb.2023.3339972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.
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Kaszecki E, Palberg D, Grant M, Griffin S, Dhanjal C, Capperauld M, Emery RJN, Saville BJ. Euglena mutabilis exists in a FAB consortium with microbes that enhance cadmium tolerance. Int Microbiol 2024:10.1007/s10123-023-00474-7. [PMID: 38167969 DOI: 10.1007/s10123-023-00474-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Synthetic algal-fungal and algal-bacterial cultures have been investigated as a means to enhance the technological applications of the algae. This inclusion of other microbes has enhanced growth and improved stress tolerance of the algal culture. The goal of the current study was to investigate natural microbial consortia to gain an understanding of the occurrence and benefits of these associations in nature. The photosynthetic protist Euglena mutabilis is often found in association with other microbes in acidic environments with high heavy metal (HM) concentrations. This may suggest that microbial interactions are essential for the protist's ability to tolerate these extreme environments. Our study assessed the Cd tolerance of a natural fungal-algal-bacterial (FAB) association whereby the algae is E. mutabilis. RESULTS This study provides the first assessment of antibiotic and antimycotic agents on an E. mutabilis culture. The results indicate that antibiotic and antimycotic applications significantly decreased the viability of E. mutabilis cells when they were also exposed to Cd. Similar antibiotic treatments of E. gracilis cultures had variable or non-significant impacts on Cd tolerance. E. gracilis also recovered better after pre-treatment with antibiotics and Cd than did E. mutabilis. The recoveries were assessed by heterotrophic growth without antibiotics or Cd. In contrast, both Euglena species displayed increased chlorophyll production upon Cd exposure. PacBio full-length amplicon sequencing and targeted Sanger sequencing identified the microbial species present in the E. mutabilis culture to be the fungus Talaromyces sp. and the bacterium Acidiphilium acidophilum. CONCLUSION This study uncovers a possible fungal, algal, and bacterial relationship, what we refer to as a FAB consortium. The members of this consortium interact to enhance the response to Cd exposure. This results in a E. mutabilis culture that has a higher tolerance to Cd than the axenic E. gracilis. The description of this interaction provides a basis for explore the benefits of natural interactions. This will provide knowledge and direction for use when creating or maintaining FAB interactions for biotechnological purposes, including bioremediation.
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Affiliation(s)
- Emma Kaszecki
- Environmental and Life Science Graduate Program, Trent University, Peterborough, ON, Canada
| | - Daniel Palberg
- Environmental and Life Science Graduate Program, Trent University, Peterborough, ON, Canada
| | - Mikaella Grant
- Environmental and Life Science Graduate Program, Trent University, Peterborough, ON, Canada
| | - Sarah Griffin
- Forensic Science Department, Trent University, Peterborough, ON, Canada
| | - Chetan Dhanjal
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - R J Neil Emery
- Environmental and Life Science Graduate Program, Trent University, Peterborough, ON, Canada
- Department of Biology, Trent University, Peterborough, ON, Canada
| | - Barry J Saville
- Environmental and Life Science Graduate Program, Trent University, Peterborough, ON, Canada.
- Forensic Science Department, Trent University, Peterborough, ON, Canada.
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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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Saadat NP, van Aalst M, Brand A, Ebenhöh O, Tissier A, Matuszyńska AB. Shifts in carbon partitioning by photosynthetic activity increase terpenoid synthesis in glandular trichomes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:1716-1728. [PMID: 37337787 DOI: 10.1111/tpj.16352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023]
Abstract
Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production.
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Affiliation(s)
- Nima P Saadat
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Marvin van Aalst
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Alejandro Brand
- Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Oliver Ebenhöh
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Alain Tissier
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Anna B Matuszyńska
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
- Computational Life Science, Department of Biology, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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Fakih I, Got J, Robles-Rodriguez CE, Siegel A, Forano E, Muñoz-Tamayo R. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85. mSystems 2023; 8:e0102722. [PMID: 37289026 PMCID: PMC10308913 DOI: 10.1128/msystems.01027-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023] Open
Abstract
Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.
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Affiliation(s)
- Ibrahim Fakih
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - Jeanne Got
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | | | - Anne Siegel
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | - Evelyne Forano
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
| | - Rafael Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
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8
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Jenior ML, Glass EM, Papin JA. Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling. Bioinformatics 2023; 39:btad367. [PMID: 37279743 PMCID: PMC10275916 DOI: 10.1093/bioinformatics/btad367] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
MOTIVATION Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (i) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (ii) lack effective network curation tools, (iii) are not sufficiently user-friendly, and (iv) often produce low-quality draft reconstructions. RESULTS Here, we present Reconstructor, a user-friendly, COBRApy-compatible tool that produces high-quality draft reconstructions with reaction and metabolite naming conventions that are consistent with the ModelSEED biochemistry database and includes a gap-filling technique based on the principles of parsimony. Reconstructor can generate SBML GENREs from three input types: annotated protein .fasta sequences (Type 1 input), a BLASTp output (Type 2), or an existing SBML GENRE that can be further gap-filled (Type 3). While Reconstructor can be used to create GENREs of any species, we demonstrate the utility of Reconstructor with bacterial reconstructions. We demonstrate how Reconstructor readily generates high-quality GENRES that capture strain, species, and higher taxonomic differences in functional metabolism of bacteria and are useful for further biological discovery. AVAILABILITY AND IMPLEMENTATION The Reconstructor Python package is freely available for download. Complete installation and usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
| | - Emma M Glass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States
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9
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Chen C, Liao C, Liu YY. Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning. Nat Commun 2023; 14:2375. [PMID: 37185345 PMCID: PMC10130184 DOI: 10.1038/s41467-023-38110-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method - CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) - to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.
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Affiliation(s)
- Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
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Inwongwan S, Pekkoh J, Pumas C, Sattayawat P. Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications. Front Microbiol 2023; 14:1143770. [PMID: 36937274 PMCID: PMC10018167 DOI: 10.3389/fmicb.2023.1143770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.
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Affiliation(s)
- Sahutchai Inwongwan
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Jeeraporn Pekkoh
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Chayakorn Pumas
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Pachara Sattayawat
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
- *Correspondence: Pachara Sattayawat,
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Calhoun S, Kamel B, Bell TA, Kruse CP, Riley R, Singan V, Kunde Y, Gleasner CD, Chovatia M, Sandor L, Daum C, Treen D, Bowen BP, Louie KB, Northen TR, Starkenburg SR, Grigoriev IV. Multi-omics profiling of the cold tolerant Monoraphidium minutum 26B-AM in response to abiotic stress. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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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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13
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Mataigne V, Vannier N, Vandenkoornhuyse P, Hacquard S. Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes. Front Microbiol 2022; 12:780469. [PMID: 34987488 PMCID: PMC8721230 DOI: 10.3389/fmicb.2021.780469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 11/25/2021] [Indexed: 12/26/2022] Open
Abstract
Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.
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Affiliation(s)
- Victor Mataigne
- Université de Rennes 1, CNRS, UMR6553 ECOBIO, Rennes, France.,Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Nathan Vannier
- Université de Rennes 1, CNRS, UMR6553 ECOBIO, Rennes, France
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14
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15
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Vijayakumar S, Angione C. Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002. STAR Protoc 2021; 2:100837. [PMID: 34632416 PMCID: PMC8488602 DOI: 10.1016/j.xpro.2021.100837] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).
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Affiliation(s)
- Supreeta Vijayakumar
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
| | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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16
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Esvap E, Ulgen KO. Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome. ACS Synth Biol 2021; 10:2121-2137. [PMID: 34402617 DOI: 10.1021/acssynbio.1c00140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.
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Affiliation(s)
- Elif Esvap
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
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17
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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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18
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Zimmermann J, Kaleta C, Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 2021; 22:81. [PMID: 33691770 PMCID: PMC7949252 DOI: 10.1186/s13059-021-02295-1] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 02/10/2021] [Indexed: 12/21/2022] Open
Abstract
Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.
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Affiliation(s)
- Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Christoph Kaleta
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Silvio Waschina
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
- Christian-Albrechts-University Kiel, Institute of Human Nutrition and Food Science, Nutriinformatics, Heinrich-Hecht-Platz 10, Kiel, 24118 Germany
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19
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Ofaim S, Sulheim S, Almaas E, Sher D, Segrè D. Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus. Front Genet 2021; 12:586293. [PMID: 33633777 PMCID: PMC7900632 DOI: 10.3389/fgene.2021.586293] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 01/14/2021] [Indexed: 12/02/2022] Open
Abstract
Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.
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Affiliation(s)
- Shany Ofaim
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,Department of Marine Biology, University of Haifa, Haifa, Israel
| | - Snorre Sulheim
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,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 Sher
- Department of Marine Biology, University of Haifa, Haifa, Israel
| | - Daniel Segrè
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,Department of Biomedical Engineering, Boston University, Boston, MA, United States.,Department of Physics, Boston University, Boston, MA, United States.,Department of Biology, Boston University, Boston, MA, United States
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20
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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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21
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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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22
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Dittami SM, Corre E, Brillet-Guéguen L, Lipinska AP, Pontoizeau N, Aite M, Avia K, Caron C, Cho CH, Collén J, Cormier A, Delage L, Doubleau S, Frioux C, Gobet A, González-Navarrete I, Groisillier A, Hervé C, Jollivet D, KleinJan H, Leblanc C, Liu X, Marie D, Markov GV, Minoche AE, Monsoor M, Pericard P, Perrineau MM, Peters AF, Siegel A, Siméon A, Trottier C, Yoon HS, Himmelbauer H, Boyen C, Tonon T. The genome of Ectocarpus subulatus - A highly stress-tolerant brown alga. Mar Genomics 2020; 52:100740. [PMID: 31937506 DOI: 10.1016/j.margen.2020.100740] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 01/01/2020] [Indexed: 11/20/2022]
Abstract
Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.
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Affiliation(s)
- Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France.
| | - Erwan Corre
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Loraine Brillet-Guéguen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Agnieszka P Lipinska
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Noé Pontoizeau
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Meziane Aite
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France
| | - Komlan Avia
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Université de Strasbourg, INRA, SVQV UMR-A 1131, F-68000 Colmar, France
| | - Christophe Caron
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Chung Hyun Cho
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Alexandre Cormier
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Sylvie Doubleau
- IRD, UMR DIADE, 911 Avenue Agropolis, BP 64501, 34394 Montpellier, France
| | | | - Angélique Gobet
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Irene González-Navarrete
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Agnès Groisillier
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Cécile Hervé
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Didier Jollivet
- Sorbonne Université, CNRS, Adaptation and Diversity in the Marine Environment (ADME), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Hetty KleinJan
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Xi Liu
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Dominique Marie
- Sorbonne Université, CNRS, Adaptation and Diversity in the Marine Environment (ADME), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - André E Minoche
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Misharl Monsoor
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Pierre Pericard
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Marie-Mathilde Perrineau
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Scottish Association for Marine Science, Scottish Marine Institute, Oban PA37 1QA, United Kingdom
| | | | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France
| | - Amandine Siméon
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Camille Trottier
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France; Laboratory of Digital Sciences of Nantes (LS2N) - University of Nantes, France
| | - Hwan Su Yoon
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Heinz Himmelbauer
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; Department of Biotechnology, University of Natural Resources and Life Sciences (BOKU), Vienna, 1190 Vienna, Austria
| | - Catherine Boyen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Thierry Tonon
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Centre for Novel Agricultural Products, Department of Biology, University of York, York YO10 5DD, United Kingdom
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23
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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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Frioux C, Singh D, Korcsmaros T, Hildebrand F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput Struct Biotechnol J 2020; 18:1722-1734. [PMID: 32670511 PMCID: PMC7347713 DOI: 10.1016/j.csbj.2020.06.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.
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Affiliation(s)
- Clémence Frioux
- Inria, CNRS, INRAE Bordeaux, France
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Dipali Singh
- Microbes in the Food Chain, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
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25
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Belcour A, Girard J, Aite M, Delage L, Trottier C, Marteau C, Leroux C, Dittami SM, Sauleau P, Corre E, Nicolas J, Boyen C, Leblanc C, Collén J, Siegel A, Markov GV. Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift. iScience 2020; 23:100849. [PMID: 32058961 PMCID: PMC6997860 DOI: 10.1016/j.isci.2020.100849] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 11/11/2019] [Accepted: 01/13/2020] [Indexed: 11/03/2022] Open
Abstract
Inferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. Therefore, specific bioinformatic tools are necessary to infer biochemical reactions and metabolic structures that can be checked experimentally. Using an integrative approach combining genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterols or mycosporine-like amino acid synthesis pathways, undergo substantial turnover. This phenomenon, here formally defined as "metabolic pathway drift," is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a methodological approach to formalize the logical reasoning necessary to infer reactions and molecular structures, abstracting molecular transformations based on previous biochemical knowledge.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Jean Girard
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | | | - Cédric Leroux
- Sorbonne Université, CNRS, Plateforme METABOMER-Corsaire (FR2424), Station Biologique de Roscoff, Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Erwan Corre
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, Roscoff, France
| | - Jacques Nicolas
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Catherine Boyen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France.
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26
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Nègre D, Aite M, Belcour A, Frioux C, Brillet-Guéguen L, Liu X, Bordron P, Godfroy O, Lipinska AP, Leblanc C, Siegel A, Dittami SM, Corre E, Markov GV. Genome-Scale Metabolic Networks Shed Light on the Carotenoid Biosynthesis Pathway in the Brown Algae Saccharina japonica and Cladosiphon okamuranus. Antioxidants (Basel) 2019; 8:E564. [PMID: 31744163 PMCID: PMC6912245 DOI: 10.3390/antiox8110564] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/13/2019] [Accepted: 11/15/2019] [Indexed: 12/20/2022] Open
Abstract
Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome-Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.
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Affiliation(s)
- Delphine Nègre
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 9, Rue Bias, 44035 Nantes, France
| | - Méziane Aite
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Arnaud Belcour
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Clémence Frioux
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
- Quadram Institute, Colney Lane, Norwich NR4 7UQ, UK
| | - Loraine Brillet-Guéguen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Xi Liu
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Philippe Bordron
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Olivier Godfroy
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Agnieszka P. Lipinska
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Simon M. Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Erwan Corre
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Gabriel V. Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
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27
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Frioux C, Fremy E, Trottier C, Siegel A. Scalable and exhaustive screening of metabolic functions carried out by microbial consortia. Bioinformatics 2019; 34:i934-i943. [PMID: 30423063 PMCID: PMC6129287 DOI: 10.1093/bioinformatics/bty588] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Motivation The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far. Results We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria. Availability and implementation Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.
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Affiliation(s)
| | - Enora Fremy
- Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | | | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, Rennes, France
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28
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Tiukova IA, Prigent S, Nielsen J, Sandgren M, Kerkhoven EJ. Genome‐scale model of
Rhodotorula toruloides
metabolism. Biotechnol Bioeng 2019; 116:3396-3408. [DOI: 10.1002/bit.27162] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 08/08/2019] [Accepted: 09/05/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Ievgeniia A. Tiukova
- Systems and Synthetic Biology, Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburg Sweden
- Department of Molecular SciencesSwedish University of Agricultural SciencesUppsala Sweden
| | | | - Jens Nielsen
- Systems and Synthetic Biology, Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburg Sweden
| | - Mats Sandgren
- Department of Molecular SciencesSwedish University of Agricultural SciencesUppsala Sweden
| | - Eduard J. Kerkhoven
- Systems and Synthetic Biology, Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburg Sweden
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29
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Cottret L, Frainay C, Chazalviel M, Cabanettes F, Gloaguen Y, Camenen E, Merlet B, Heux S, Portais JC, Poupin N, Vinson F, Jourdan F. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res 2019; 46:W495-W502. [PMID: 29718355 PMCID: PMC6030842 DOI: 10.1093/nar/gky301] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/11/2018] [Indexed: 12/16/2022] Open
Abstract
Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.
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Affiliation(s)
- Ludovic Cottret
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | | | - Maxime Chazalviel
- INRA, UMR1331, Toxalim, F-31000 Toulouse, France.,MedDay Pharmaceuticals, Paris, France
| | | | - Yoann Gloaguen
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany.,Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany.,Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
| | | | | | - Stéphanie Heux
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France.,INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France.,CNRS, UMR5504, F-31400 Toulouse, France
| | - Jean-Charles Portais
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France.,INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France.,CNRS, UMR5504, F-31400 Toulouse, France
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30
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van Steijn L, Verbeek FJ, Spaink HP, Merks RM. Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio. Zebrafish 2019; 16:348-362. [PMID: 31216234 PMCID: PMC6822484 DOI: 10.1089/zeb.2018.1712] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.
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Affiliation(s)
| | - Fons J. Verbeek
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
| | - Herman P. Spaink
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
| | - Roeland M.H. Merks
- Mathematical Institute, Leiden University, Leiden, Netherlands
- Institute of Biology Leiden, Leiden University, Leiden, Netherlands
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31
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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32
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Saa PA, Cortés MP, López J, Bustos D, Maass A, Agosin E. Expanding Metabolic Capabilities Using Novel Pathway Designs: Computational Tools and Case Studies. Biotechnol J 2019; 14:e1800734. [DOI: 10.1002/biot.201800734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/22/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Pedro A. Saa
- Departamento de Ingeniería Química y BioprocesosPontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 7820436 Santiago Chile
| | - María P. Cortés
- Centro de Modelamiento MatemáticoUniversidad de Chile Av. Beaucheff 851 Santiago 8370456 Chile
- Centro de Regulación del GenomaUniversidad de Chile Av. Beaucheff 851 Santiago 8370456 Chile
| | - Javiera López
- Centro de Aromas y SaboresDICTUC S.A Av. Vicuña Mackenna 4860 Santiago 7820436 Chile
| | - Diego Bustos
- Centro de Aromas y SaboresDICTUC S.A Av. Vicuña Mackenna 4860 Santiago 7820436 Chile
| | - Alejandro Maass
- Centro de Modelamiento MatemáticoUniversidad de Chile Av. Beaucheff 851 Santiago 8370456 Chile
- Departmento de Ingeniería MatemáticaUniversidad de Chile Av. Beaucheff 851 Santiago 8370456 Chile
| | - Eduardo Agosin
- Departamento de Ingeniería Química y BioprocesosPontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 7820436 Santiago Chile
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33
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Inwongwan S, Kruger NJ, Ratcliffe RG, O'Neill EC. Euglena Central Metabolic Pathways and Their Subcellular Locations. Metabolites 2019; 9:E115. [PMID: 31207935 PMCID: PMC6630311 DOI: 10.3390/metabo9060115] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/03/2019] [Accepted: 06/11/2019] [Indexed: 01/16/2023] Open
Abstract
Euglenids are a group of algae of great interest for biotechnology, with a large and complex metabolic capability. To study the metabolic network, it is necessary to know where the component enzymes are in the cell, but despite a long history of research into Euglena, the subcellular locations of many major pathways are only poorly defined. Euglena is phylogenetically distant from other commonly studied algae, they have secondary plastids bounded by three membranes, and they can survive after destruction of their plastids. These unusual features make it difficult to assume that the subcellular organization of the metabolic network will be equivalent to that of other photosynthetic organisms. We analysed bioinformatic, biochemical, and proteomic information from a variety of sources to assess the subcellular location of the enzymes of the central metabolic pathways, and we use these assignments to propose a model of the metabolic network of Euglena. Other than photosynthesis, all major pathways present in the chloroplast are also present elsewhere in the cell. Our model demonstrates how Euglena can synthesise all the metabolites required for growth from simple carbon inputs, and can survive in the absence of chloroplasts.
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Affiliation(s)
- Sahutchai Inwongwan
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
| | - Nicholas J Kruger
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
| | - Ellis C O'Neill
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
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34
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Daval S, Belcour A, Gazengel K, Legrand L, Gouzy J, Cottret L, Lebreton L, Aigu Y, Mougel C, Manzanares-Dauleux MJ. Computational analysis of the Plasmodiophora brassicae genome: mitochondrial sequence description and metabolic pathway database design. Genomics 2018; 111:1629-1640. [PMID: 30447277 DOI: 10.1016/j.ygeno.2018.11.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 09/23/2018] [Accepted: 11/12/2018] [Indexed: 10/27/2022]
Abstract
Plasmodiophora brassicae is an obligate biotrophic pathogenic protist responsible for clubroot, a root gall disease of Brassicaceae species. In addition to the reference genome of the P. brassicae European e3 isolate and the draft genomes of Canadian or Chinese isolates, we present the genome of eH, a second European isolate. Refinement of the annotation of the eH genome led to the identification of the mitochondrial genome sequence, which was found to be bigger than that of Spongospora subterranea, another plant parasitic Plasmodiophorid phylogenetically related to P. brassicae. New pathways were also predicted, such as those for the synthesis of spermidine, a polyamine up-regulated in clubbed regions of roots. A P. brassicae pathway genome database was created to facilitate the functional study of metabolic pathways in transcriptomics approaches. These available tools can help in our understanding of the regulation of P. brassicae metabolism during infection and in response to diverse constraints.
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Affiliation(s)
- Stéphanie Daval
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France.
| | - Arnaud Belcour
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France
| | - Kévin Gazengel
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France
| | - Ludovic Legrand
- LIPM, INRA, CNRS, Université de Toulouse, Castanet Tolosan, France
| | - Jérôme Gouzy
- LIPM, INRA, CNRS, Université de Toulouse, Castanet Tolosan, France
| | - Ludovic Cottret
- LIPM, INRA, CNRS, Université de Toulouse, Castanet Tolosan, France
| | - Lionel Lebreton
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France
| | - Yoann Aigu
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France
| | - Christophe Mougel
- IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Domaine de la Motte, Le Rheu F-35653, France
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Prigent S, Nielsen JC, Frisvad JC, Nielsen J. Reconstruction of 24 Penicillium genome-scale metabolic models shows diversity based on their secondary metabolism. Biotechnol Bioeng 2018; 115:2604-2612. [PMID: 29873086 DOI: 10.1002/bit.26739] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 04/06/2018] [Accepted: 05/30/2018] [Indexed: 11/11/2022]
Abstract
Modeling of metabolism at the genome-scale has proved to be an efficient method for explaining the phenotypic traits observed in living organisms. Further, it can be used as a means of predicting the effect of genetic modifications for example, development of microbial cell factories. With the increasing amount of genome sequencing data available, there exists a need to accurately and efficiently generate such genome-scale metabolic models (GEMs) of nonmodel organisms, for which data is sparse. In this study, we present an automatic reconstruction approach applied to 24 Penicillium species, which have potential for production of pharmaceutical secondary metabolites or use in the manufacturing of food products, such as cheeses. The models were based on the MetaCyc database and a previously published Penicillium GEM and gave rise to comprehensive genome-scale metabolic descriptions. The models proved that while central carbon metabolism is highly conserved, secondary metabolic pathways represent the main diversity among species. The automatic reconstruction approach presented in this study can be applied to generate GEMs of other understudied organisms, and the developed GEMs are a useful resource for the study of Penicillium metabolism, for example, for the scope of developing novel cell factories.
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Affiliation(s)
- Sylvain Prigent
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens C Nielsen
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens C Frisvad
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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36
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Aite M, Chevallier M, Frioux C, Trottier C, Got J, Cortés MP, Mendoza SN, Carrier G, Dameron O, Guillaudeux N, Latorre M, Loira N, Markov GV, Maass A, Siegel A. Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models. PLoS Comput Biol 2018; 14:e1006146. [PMID: 29791443 PMCID: PMC5988327 DOI: 10.1371/journal.pcbi.1006146] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 06/05/2018] [Accepted: 04/17/2018] [Indexed: 11/27/2022] Open
Abstract
Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.
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Affiliation(s)
| | - Marie Chevallier
- IRISA, Univ Rennes, Inria, CNRS, Rennes, France
- ECOBIO, Univ Rennes, CNRS, Rennes, France
| | | | - Camille Trottier
- IRISA, Univ Rennes, Inria, CNRS, Rennes, France
- UMR 6004 ComBi, Université de Nantes, CNRS, Nantes, France
| | - Jeanne Got
- IRISA, Univ Rennes, Inria, CNRS, Rennes, France
| | - María Paz Cortés
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Chile
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile
- Centro para la Regulación del Genoma (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Sebastián N. Mendoza
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Chile
- Centro para la Regulación del Genoma (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Grégory Carrier
- Laboratoire de Physiologie et de Biotechnologie des Algues, IFREMER, Nantes, France
| | | | | | - Mauricio Latorre
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Chile
- Centro para la Regulación del Genoma (Fondap 15090007), Universidad de Chile, Santiago, Chile
- Instituto de ciencias de la ingeniería, Universidad de O'Higgins, Rancagua, Chile
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Nicolás Loira
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Chile
- Centro para la Regulación del Genoma (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Gabriel V. Markov
- UMR 8227, Integrative Biology of Marine Models, Station biologique de Roscoff, Sorbonne Université, CNRS, Roscoff, France
| | - Alejandro Maass
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Chile
- Centro para la Regulación del Genoma (Fondap 15090007), Universidad de Chile, Santiago, Chile
| | - Anne Siegel
- IRISA, Univ Rennes, Inria, CNRS, Rennes, France
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Latendresse M, Karp PD. Evaluation of reaction gap-filling accuracy by randomization. BMC Bioinformatics 2018; 19:53. [PMID: 29444634 PMCID: PMC5813426 DOI: 10.1186/s12859-018-2050-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 01/31/2018] [Indexed: 12/18/2022] Open
Abstract
Background Completion of genome-scale flux-balance models using computational reaction gap-filling is a widely used approach, but its accuracy is not well known. Results We report on computational experiments of reaction gap filling in which we generated degraded versions of the EcoCyc-20.0-GEM model by randomly removing flux-carrying reactions from a growing model. We gap-filled the degraded models and compared the resulting gap-filled models with the original model. Gap-filling was performed by the Pathway Tools MetaFlux software using its General Development Mode (GenDev) and its Fast Development Mode (FastDev). We explored 12 GenDev variants including two linear solvers (SCIP and CPLEX) for solving the Mixed Integer Linear Programming (MILP) problems for gap filling; three different sets of linear constraints were applied; and two MILP methods were implemented. We compared these 13 variants according to accuracy, speed, and amount of information returned to the user. Conclusions We observed large variation among the performance of the 13 gap-filling variants. Although no variant was best in all dimensions, we found one variant that was fast, accurate, and returned more information to the user. Some gap-filling variants were inaccurate, producing solutions that were non-minimum or invalid (did not enable model growth). The best GenDev variant showed a best average precision of 87% and a best average recall of 61%. FastDev showed an average precision of 71% and an average recall of 59%. Thus, using the most accurate variant, approximately 13% of the gap-filled reactions were incorrect (were not the reactions removed from the model), and 39% of gap-filled reactions were not found, suggesting that curation is still an important aspect of metabolic-model development.
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Affiliation(s)
- Mario Latendresse
- SRI International/Artificial Intelligence Center, 333 Ravenswood Ave, Menlo Park, 94025, USA.
| | - Peter D Karp
- SRI International/Artificial Intelligence Center, 333 Ravenswood Ave, Menlo Park, 94025, USA
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38
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Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. Curr Opin Biotechnol 2017; 51:103-108. [PMID: 29278837 DOI: 10.1016/j.copbio.2017.12.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 12/18/2022]
Abstract
With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.
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39
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Cortés MP, Mendoza SN, Travisany D, Gaete A, Siegel A, Cambiazo V, Maass A. Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing. Front Microbiol 2017; 8:2462. [PMID: 29321769 PMCID: PMC5732189 DOI: 10.3389/fmicb.2017.02462] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/27/2017] [Indexed: 01/27/2023] Open
Abstract
Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen's biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.
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Affiliation(s)
- María P Cortés
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.,Fondap Center for Genome Regulation (CGR), Santiago, Chile
| | - Sebastián N Mendoza
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Fondap Center for Genome Regulation (CGR), Santiago, Chile
| | - Dante Travisany
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.,Fondap Center for Genome Regulation (CGR), Santiago, Chile
| | - Alexis Gaete
- Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Anne Siegel
- DYLISS (INRIA-IRISA)-INRIA, CNRS UMR 6074, Université de Rennes 1, Rennes, France
| | - Verónica Cambiazo
- Fondap Center for Genome Regulation (CGR), Santiago, Chile.,Laboratorio de Bioinformática y Expresión Génica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Alejandro Maass
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Fondap Center for Genome Regulation (CGR), Santiago, Chile.,Department of Mathematical Engineering, Universidad de Chile, Santiago, Chile
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40
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Laniau J, Frioux C, Nicolas J, Baroukh C, Cortes MP, Got J, Trottier C, Eveillard D, Siegel A. Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks. PeerJ 2017; 5:e3860. [PMID: 29038751 PMCID: PMC5641430 DOI: 10.7717/peerj.3860] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 09/06/2017] [Indexed: 12/04/2022] Open
Abstract
Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.
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Affiliation(s)
- Julie Laniau
- Institut de Recherche en Informatique et Systèmes Aléatoires, Centre National de la Recherche Scientifique, Rennes, France.,DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France
| | - Clémence Frioux
- Institut de Recherche en Informatique et Systèmes Aléatoires, Centre National de la Recherche Scientifique, Rennes, France.,DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France
| | - Jacques Nicolas
- Institut de Recherche en Informatique et Systèmes Aléatoires, Centre National de la Recherche Scientifique, Rennes, France.,DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France
| | - Caroline Baroukh
- Laboratoire des Interactions Plantes Micro-organismes, Institut National de la Recherche en Agonomie, Castanet-Tolosan, France
| | - Maria-Paz Cortes
- Center of Mathematical Modelling, Universidad de Chile, Santiago, Chile
| | - Jeanne Got
- Institut de Recherche en Informatique et Systèmes Aléatoires, Centre National de la Recherche Scientifique, Rennes, France.,DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France
| | - Camille Trottier
- DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France.,Laboratoire des Sciences du Numérique de Nantes, Université de Nantes, Nantes, France
| | - Damien Eveillard
- Laboratoire des Sciences du Numérique de Nantes, Université de Nantes, Nantes, France
| | - Anne Siegel
- Institut de Recherche en Informatique et Systèmes Aléatoires, Centre National de la Recherche Scientifique, Rennes, France.,DYLISS, Institut National de Recherche en Informatique et Automatique, Rennes, France
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Rejc Ž, Magdevska L, Tršelič T, Osolin T, Vodopivec R, Mraz J, Pavliha E, Zimic N, Cvitanović T, Rozman D, Moškon M, Mraz M. Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures. Comput Biol Med 2017; 88:150-160. [PMID: 28732234 DOI: 10.1016/j.compbiomed.2017.07.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 01/30/2023]
Abstract
Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.
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Affiliation(s)
- Živa Rejc
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Lidija Magdevska
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tilen Tršelič
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Timotej Osolin
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Rok Vodopivec
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Jakob Mraz
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Eva Pavliha
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tanja Cvitanović
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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42
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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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