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Pathom-Aree W, Sattayawat P, Inwongwan S, Cheirsilp B, Liewtrakula N, Maneechote W, Rangseekaew P, Ahmad F, Mehmood MA, Gao F, Srinuanpan S. Microalgae growth-promoting bacteria for cultivation strategies: Recent updates and progress. Microbiol Res 2024; 286:127813. [PMID: 38917638 DOI: 10.1016/j.micres.2024.127813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/02/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024]
Abstract
Microalgae growth-promoting bacteria (MGPB), both actinobacteria and non-actinobacteria, have received considerable attention recently because of their potential to develop microalgae-bacteria co-culture strategies for improved efficiency and sustainability of the water-energy-environment nexus. Owing to their diverse metabolic pathways and ability to adapt to diverse conditions, microalgal-MGPB co-cultures could be promising biological systems under uncertain environmental and nutrient conditions. This review proposes the recent updates and progress on MGPB for microalgae cultivation through co-culture strategies. Firstly, potential MGPB strains for microalgae cultivation are introduced. Following, microalgal-MGPB interaction mechanisms and applications of their co-cultures for biomass production and wastewater treatment are reviewed. Moreover, state-of-the-art studies on synthetic biology and metabolic network analysis, along with the challenges and prospects of opting these approaches for microalgal-MGPB co-cultures are presented. It is anticipated that these strategies may significantly improve the sustainability of microalgal-MGPB co-cultures for wastewater treatment, biomass valorization, and bioproducts synthesis in a circular bioeconomy paradigm.
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Affiliation(s)
- Wasu Pathom-Aree
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; Center of Excellence in Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Pachara Sattayawat
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; Center of Excellence in Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sahutchai Inwongwan
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; Center of Excellence in Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Benjamas Cheirsilp
- Program of Biotechnology, Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources, Faculty of Agro-Industry, Prince of Songkla University, Songkhla 90110, Thailand
| | - Naruepon Liewtrakula
- Program of Biotechnology, Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources, Faculty of Agro-Industry, Prince of Songkla University, Songkhla 90110, Thailand
| | - Wageeporn Maneechote
- Program of Biotechnology, Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources, Faculty of Agro-Industry, Prince of Songkla University, Songkhla 90110, Thailand; Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Pharada Rangseekaew
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; Center of Excellence in Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Fiaz Ahmad
- Key Laboratory for Space Bioscience & Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Muhammad Aamer Mehmood
- Bioenergy Research Center, Department of Bioinformatics & Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan
| | - Fengzheng Gao
- Sustainable Food Processing Laboratory, Institute of Food, Nutrition and Health, ETH Zurich, Zurich 8092, Switzerland; Laboratory of Nutrition and Metabolic Epigenetics, Institute of Food, Nutrition and Health, ETH Zurich, Schwerzenbach 8603, Switzerland
| | - Sirasit Srinuanpan
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; Center of Excellence in Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand; Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand; Biorefinery and Bioprocess Engineering Research Cluster, Chiang Mai University, Chiang Mai 50200, Thailand.
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2
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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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3
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Fernie AR, Yan J, Aharoni A, Ma J. Editorial: The past, present and future of The Plant Journal Resource Articles. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:967-973. [PMID: 37943112 DOI: 10.1111/tpj.16515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Affiliation(s)
- Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Jianbing Yan
- National Key Laboratory of Crop Genetics, Huazhong Agricultural District, Wuhan, China
| | - Asaph Aharoni
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Jianxian Ma
- Purdue University, 915 S. University St, West Lafayette, IN, USA
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4
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Wendering P, Nikoloski Z. Toward mechanistic modeling and rational engineering of plant respiration. PLANT PHYSIOLOGY 2023; 191:2150-2166. [PMID: 36721968 PMCID: PMC10069892 DOI: 10.1093/plphys/kiad054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.
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Affiliation(s)
- Philipp Wendering
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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5
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Rodenburg SYA, Seidl MF, de Ridder D, Govers F. Uncovering the Role of Metabolism in Oomycete-Host Interactions Using Genome-Scale Metabolic Models. Front Microbiol 2021; 12:748178. [PMID: 34707596 PMCID: PMC8543037 DOI: 10.3389/fmicb.2021.748178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the "mass-flow" through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host-pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.
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Affiliation(s)
- Sander Y. A. Rodenburg
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
- Bioinformatics Group, Wageningen University & Research, Wageningen, Netherlands
| | - Michael F. Seidl
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
- Theoretical Biology & Bioinformatics group, Department of Biology, Utrecht University, Wageningen, Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University & Research, Wageningen, Netherlands
| | - Francine Govers
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
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6
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DeWeese KJ, Osborne MG. Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement. JOURNAL OF THE WORLD AQUACULTURE SOCIETY 2021; 52:1009-1030. [PMID: 34732977 PMCID: PMC8562568 DOI: 10.1111/jwas.12782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/25/2021] [Indexed: 06/01/2023]
Abstract
"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.
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Affiliation(s)
- Kelly J DeWeese
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, California, Los Angeles
| | - Melisa G Osborne
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, California, Los Angeles
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7
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Karimi E, Geslain E, Belcour A, Frioux C, Aïte M, Siegel A, Corre E, Dittami SM. Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines. PeerJ 2021; 9:e11344. [PMID: 33996285 PMCID: PMC8106915 DOI: 10.7717/peerj.11344] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/03/2021] [Indexed: 01/29/2023] Open
Abstract
Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.
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Affiliation(s)
- Elham Karimi
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Enora Geslain
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France.,FR2424, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Arnaud Belcour
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | | | - Méziane Aïte
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Anne Siegel
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Erwan Corre
- FR2424, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Simon M Dittami
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
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8
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Abstract
Model organisms are extensively used in research as accessible and convenient systems for studying a particular area or question in biology. Traditionally, only a limited number of organisms have been studied in detail, but modern genomic tools are enabling researchers to extend beyond the set of classical model organisms to include novel species from less-studied phylogenetic groups. This review focuses on model species for an important group of multicellular organisms, the brown algae. The development of genetic and genomic tools for the filamentous brown alga Ectocarpus has led to it emerging as a general model system for this group, but additional models, such as Fucus or Dictyota dichotoma, remain of interest for specific biological questions. In addition, Saccharina japonica has emerged as a model system to directly address applied questions related to algal aquaculture. We discuss the past, present, and future of brown algal model organisms in relation to the opportunities and challenges in brown algal research.
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Affiliation(s)
- Susana M Coelho
- Laboratory of Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), CNRS, Sorbonne Université, 29680 Roscoff, France;
- Current affiliation: Department of Algal Development and Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany;
| | - J Mark Cock
- Laboratory of Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), CNRS, Sorbonne Université, 29680 Roscoff, France;
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9
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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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10
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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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11
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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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12
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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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13
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Guillot L, Delage L, Viari A, Vandenbrouck Y, Com E, Ritter A, Lavigne R, Marie D, Peterlongo P, Potin P, Pineau C. Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes. BMC Genomics 2019; 20:56. [PMID: 30654742 PMCID: PMC6337836 DOI: 10.1186/s12864-019-5431-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 01/03/2019] [Indexed: 01/02/2023] Open
Abstract
Background Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes. Results Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data. Conclusions Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu. Data are available via ProteomeXchange under identifier PXD010618. Electronic supplementary material The online version of this article (10.1186/s12864-019-5431-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Laetitia Guillot
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Ludovic Delage
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Alain Viari
- INRIA Grenoble-Rhône-Alpes, F-38330, Montbonnot-Saint-Martin, France
| | - Yves Vandenbrouck
- University Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000, Grenoble, France
| | - Emmanuelle Com
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Andrés Ritter
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.,Present address: Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratory of Computational and Quantitative Biology, F-75005, Paris, France
| | - Régis Lavigne
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Dominique Marie
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | | | - Philippe Potin
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Charles Pineau
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France. .,Protim, Univ Rennes, F-35042, Rennes cedex, France.
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14
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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: 58] [Impact Index Per Article: 9.7] [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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15
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Shah AR, Ahmad A, Srivastava S, Jaffar Ali B. Reconstruction and analysis of a genome-scale metabolic model of Nannochloropsis gaditana. ALGAL RES 2017. [DOI: 10.1016/j.algal.2017.08.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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16
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Loira N, Mendoza S, Paz Cortés M, Rojas N, Travisany D, Genova AD, Gajardo N, Ehrenfeld N, Maass A. Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production. BMC SYSTEMS BIOLOGY 2017; 11:66. [PMID: 28676050 PMCID: PMC5496344 DOI: 10.1186/s12918-017-0441-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 06/09/2017] [Indexed: 11/10/2022]
Abstract
Background Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0441-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicolás Loira
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile. .,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile.
| | - Sebastian Mendoza
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - María Paz Cortés
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile.,Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Santiago, Chile
| | - Natalia Rojas
- Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Dante Travisany
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Alex Di Genova
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Natalia Gajardo
- Centro de Investigación Austral Biotech, Universidad Santo Tomás, Avenida Ejercito 146, Santiago, Chile
| | - Nicole Ehrenfeld
- Centro de Investigación Austral Biotech, Universidad Santo Tomás, Avenida Ejercito 146, Santiago, Chile
| | - Alejandro Maass
- Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile.,Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
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17
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Proteomic approaches in microalgae: perspectives and applications. 3 Biotech 2017; 7:197. [PMID: 28667637 DOI: 10.1007/s13205-017-0831-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 05/19/2017] [Indexed: 12/13/2022] Open
Abstract
Biofuels are the promising sources which are produced by various microalgae or in the form of metabolic by-products from organic or food waste products. Microalgae have been widely reported for the production of biofuels since these have a high storage of lipids as triacylglycerides, which can mainly be converted into biofuels. Recently, products such as biodiesel, bioethanol and biogas have renewed the interest toward the microalgae. The proteomics alone will not pave the way toward finding an ideal alga which will fulfill the current energy demands, but a combined approach of proteomics, genomics and bioinformatics can be pivotal for a sustainable solution. The present review emphasizes various technologies currently involved in algal proteomics for the efficient production of biofuels.
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18
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Cormier A, Avia K, Sterck L, Derrien T, Wucher V, Andres G, Monsoor M, Godfroy O, Lipinska A, Perrineau MM, Van De Peer Y, Hitte C, Corre E, Coelho SM, Cock JM. Re-annotation, improved large-scale assembly and establishment of a catalogue of noncoding loci for the genome of the model brown alga Ectocarpus. THE NEW PHYTOLOGIST 2017; 214:219-232. [PMID: 27870061 DOI: 10.1111/nph.14321] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 10/08/2016] [Indexed: 05/28/2023]
Abstract
The genome of the filamentous brown alga Ectocarpus was the first to be completely sequenced from within the brown algal group and has served as a key reference genome both for this lineage and for the stramenopiles. We present a complete structural and functional reannotation of the Ectocarpus genome. The large-scale assembly of the Ectocarpus genome was significantly improved and genome-wide gene re-annotation using extensive RNA-seq data improved the structure of 11 108 existing protein-coding genes and added 2030 new loci. A genome-wide analysis of splicing isoforms identified an average of 1.6 transcripts per locus. A large number of previously undescribed noncoding genes were identified and annotated, including 717 loci that produce long noncoding RNAs. Conservation of lncRNAs between Ectocarpus and another brown alga, the kelp Saccharina japonica, suggests that at least a proportion of these loci serve a function. Finally, a large collection of single nucleotide polymorphism-based markers was developed for genetic analyses. These resources are available through an updated and improved genome database. This study significantly improves the utility of the Ectocarpus genome as a high-quality reference for the study of many important aspects of brown algal biology and as a reference for genomic analyses across the stramenopiles.
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Affiliation(s)
- Alexandre Cormier
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - Komlan Avia
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - Lieven Sterck
- Department of Plant Systems Biology, VIB, B-9052, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9000, Ghent, Belgium
- Bioinformatics Institute Ghent, Technologiepark 927, 9052, Ghent, Belgium
| | | | | | - Gwendoline Andres
- Abims Platform, CNRS-UPMC, FR2424, Station Biologique de Roscoff, CS 90074, 29688, Roscoff, France
| | - Misharl Monsoor
- Abims Platform, CNRS-UPMC, FR2424, Station Biologique de Roscoff, CS 90074, 29688, Roscoff, France
| | - Olivier Godfroy
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - Agnieszka Lipinska
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - Marie-Mathilde Perrineau
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - Yves Van De Peer
- Department of Plant Systems Biology, VIB, B-9052, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9000, Ghent, Belgium
- Bioinformatics Institute Ghent, Technologiepark 927, 9052, Ghent, Belgium
- Department of Genetics, Genomics Research Institute, University of Pretoria, 0028, Pretoria, South Africa
| | | | - Erwan Corre
- Abims Platform, CNRS-UPMC, FR2424, Station Biologique de Roscoff, CS 90074, 29688, Roscoff, France
| | - Susana M Coelho
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
| | - J Mark Cock
- Algal Genetics Group, CNRS, UMR 8227, Integrative Biology of Marine Models, Sorbonne Université, UPMC Univ Paris 06, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff, France
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19
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Prigent S, Frioux C, Dittami SM, Thiele S, Larhlimi A, Collet G, Gutknecht F, Got J, Eveillard D, Bourdon J, Plewniak F, Tonon T, Siegel A. Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks. PLoS Comput Biol 2017; 13:e1005276. [PMID: 28129330 PMCID: PMC5302834 DOI: 10.1371/journal.pcbi.1005276] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 02/10/2017] [Accepted: 11/30/2016] [Indexed: 11/18/2022] Open
Abstract
Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.
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Affiliation(s)
- Sylvain Prigent
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
- * E-mail: (AS); (SP)
| | - Clémence Frioux
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Simon M. Dittami
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, Roscoff, France
| | | | - Abdelhalim Larhlimi
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Guillaume Collet
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Fabien Gutknecht
- Molecular Genetics, Genomics and Microbiology - GMGM, Université de Strasbourg, Strasbourg, France
| | - Jeanne Got
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
| | - Damien Eveillard
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Jérémie Bourdon
- Computer Science Laboratory of Nantes Atlantique - LINA UMR6241, Université de Nantes, Nantes, France
| | - Frédéric Plewniak
- Molecular Genetics, Genomics and Microbiology - GMGM, Université de Strasbourg, Strasbourg, France
- GMGM, CNRS, Strasbourg, France
| | - Thierry Tonon
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, Roscoff, France
| | - Anne Siegel
- Institute for Research in IT and Random Systems - IRISA, Université de Rennes 1, Rennes, France
- Irisa, CNRS, Rennes, France
- Dyliss, Inria, Rennes, France
- * E-mail: (AS); (SP)
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Banerjee C, Singh PK, Shukla P. Microalgal bioengineering for sustainable energy development: Recent transgenesis and metabolic engineering strategies. Biotechnol J 2016; 11:303-14. [DOI: 10.1002/biot.201500284] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 09/15/2015] [Accepted: 01/05/2016] [Indexed: 12/11/2022]
Affiliation(s)
- Chiranjib Banerjee
- Department of Environmental Science & Engineering; Indian School of Mines; Dhanbad Jharkhand India
| | - Puneet Kumar Singh
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology; Maharshi Dayanand University; Rohtak Haryana India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology; Maharshi Dayanand University; Rohtak Haryana India
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Automatic Inference of Graph Transformation Rules Using the Cyclic Nature of Chemical Reactions. GRAPH TRANSFORMATION 2016. [DOI: 10.1007/978-3-319-40530-8_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Kim J, Fabris M, Baart G, Kim MK, Goossens A, Vyverman W, Falkowski PG, Lun DS. Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 85:161-176. [PMID: 26590126 DOI: 10.1111/tpj.13081] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2014] [Revised: 11/04/2015] [Accepted: 11/09/2015] [Indexed: 06/05/2023]
Abstract
Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome-scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock-in transformants. Here we present a genome-scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.
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Affiliation(s)
- Joomi Kim
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Michele Fabris
- Plant Functional Biology and Climate Change Cluster (C3), Faculty of Science University of Technology, Sydney, New South Wales, Australia
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
| | - Gino Baart
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
- Centre of Microbial and Plant Genetics Lab for Genetics and Genomics and Leuven Institute for Beer Research, Leuven University, Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Min K Kim
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA
| | - Alain Goossens
- Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, B-9052, Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052, Gent, Belgium
| | - Wim Vyverman
- Department of Biology, Laboratory of Protistology and Aquatic Ecology, Ghent University, B-9000, Gent, Belgium
| | - Paul G Falkowski
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Desmond S Lun
- Center for Computational and Integrative Biology and Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA
- Department of Plant Biology and Pathology, Rutgers University, New Brunswick, NJ, 08901, USA
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia, Australia
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Hlavova M, Turoczy Z, Bisova K. Improving microalgae for biotechnology — From genetics to synthetic biology. Biotechnol Adv 2015; 33:1194-203. [DOI: 10.1016/j.biotechadv.2015.01.009] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 01/11/2015] [Accepted: 01/17/2015] [Indexed: 01/01/2023]
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Bonin P, Groisillier A, Raimbault A, Guibert A, Boyen C, Tonon T. Molecular and biochemical characterization of mannitol-1-phosphate dehydrogenase from the model brown alga Ectocarpus sp. PHYTOCHEMISTRY 2015; 117:509-520. [PMID: 26232554 DOI: 10.1016/j.phytochem.2015.07.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 07/16/2015] [Accepted: 07/20/2015] [Indexed: 06/04/2023]
Abstract
The sugar alcohol mannitol is important in the food, pharmaceutical, medical and chemical industries. It is one of the most commonly occurring polyols in nature, with the exception of Archaea and animals. It has a range of physiological roles, including as carbon storage, compatible solute, and osmolyte. Mannitol is present in large amounts in brown algae, where its synthesis involved two steps: a mannitol-1-phosphate dehydrogenase (M1PDH) catalyzes a reversible reaction between fructose-6-phosphate (F6P) and mannitol-1-phosphate (M1P) (EC 1.1.1.17), and a mannitol-1-phosphatase hydrolyzes M1P to mannitol (EC 3.1.3.22). Analysis of the model brown alga Ectocarpus sp. genome provided three candidate genes for M1PDH activities. We report here the sequence analysis of Ectocarpus M1PDHs (EsM1PDHs), and the biochemical characterization of the recombinant catalytic domain of EsM1PDH1 (EsM1PDH1cat). Ectocarpus M1PDHs are representatives of a new type of modular M1PDHs among the polyol-specific long-chain dehydrogenases/reductases (PSLDRs). The N-terminal domain of EsM1PDH1 was not necessary for enzymatic activity. Determination of kinetic parameters indicated that EsM1PDH1cat displayed higher catalytic efficiency for F6P reduction compared to M1P oxidation. Both activities were influenced by NaCl concentration and inhibited by the thioreactive compound pHMB. These observations were completed by measurement of endogenous M1PDH activity and of EsM1PDH gene expression during one diurnal cycle. No significant changes in enzyme activity were monitored between day and night, although transcription of two out of three genes was altered, suggesting different levels of regulation for this key metabolic pathway in brown algal physiology.
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Affiliation(s)
- Patricia Bonin
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
| | - Agnès Groisillier
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
| | - Alice Raimbault
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
| | - Anaïs Guibert
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
| | - Catherine Boyen
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
| | - Thierry Tonon
- Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688 Roscoff cedex, France.
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