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      Analyzing sorbitol biosynthesis using a metabolic network flux model of a lichenized strain of the green microalga Diplosphaera chodatii

      research-article
      1 , 2 , , 3 , 1 , 4 , 1 , 5
      Microbiology Spectrum
      American Society for Microbiology
      microalgae, systems biology, metabolic network modeling, sorbitol, symbiosis

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          ABSTRACT

          Diplosphaera chodatii, a unicellular terrestrial microalga found either free-living or in association with lichenized fungi, protects itself from desiccation by synthesizing and accumulating low-molecular-weight carbohydrates such as sorbitol. The metabolism of this algal species and the interplay of sorbitol biosynthesis with its growth, light absorption, and carbon dioxide fixation are poorly understood. Here, we used a recently available genome assembly for D. chodatii to develop a metabolic flux model and analyze the alga’s metabolic capabilities, particularly, for sorbitol biosynthesis. The model contains 151 genes, 155 metabolites, and 194 unique metabolic reactions participating in 12 core metabolic pathways and five compartments. Both photoautotrophic and mixotrophic growths of D. chodatii were supported by the metabolic model. In the presence of glucose, mixotrophy led to higher biomass and sorbitol yields. Additionally, the model predicted increased starch biosynthesis at high light intensities during photoautotrophic growth, an indication that the “overflow hypothesis—stress-driven metabolic flux redistribution” could be applied to D. chodatii. Furthermore, the newly developed metabolic model of D. chodatii, iDco_core, captures both linear and cyclic electron flow schemes characterized in photosynthetic microorganisms and suggests a possible adaptation to fluctuating water availability during periods of desiccation. This work provides important new insights into the predicted metabolic capabilities of D. chodatii, including a potential biotechnological opportunity for industrial sorbitol biosynthesis.

          IMPORTANCE

          Lichenized green microalgae are vital components for the survival and growth of lichens in extreme environmental conditions. However, little is known about the metabolism and growth characteristics of these algae as individual microbes. This study aims to provide insights into some of the metabolic capabilities of Diplosphaera chodatii, a lichenized green microalgae, using a recently assembled and annotated genome of the alga. For that, a metabolic flux model was developed simulating the metabolism of this algal species and allowing for studying the algal growth, light absorption, and carbon dioxide fixation during both photoautotrophic and mixotrophic growth, in silico. An important capability of the new metabolic model of D. chodatii is capturing both linear and cyclic electron flow mechanisms characterized in several other microalgae. Moreover, the model predicts limits of the metabolic interplay between sorbitol biosynthesis and algal growth, which has potential applications in assisting the design of bio-based sorbitol production processes.

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          Technology development for the production of biobased products from biorefinery carbohydrates—the US Department of Energy’s “Top 10” revisited

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            High-throughput functional annotation and data mining with the Blast2GO suite

            Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.
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              A protocol for generating a high-quality genome-scale metabolic reconstruction.

              Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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                Author and article information

                Contributors
                Role: Writing – original draftRole: ConceptualizationRole: Formal analysis
                Role: Writing – review and editing
                Role: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review and editing
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                January 2025
                09 December 2024
                09 December 2024
                : 13
                : 1
                : e03660-23
                Affiliations
                [1 ]Australian National Herbarium, National Research Collections Australia, NCMI, CSIRO; , Canberra, Australia
                [2 ]Synthetic Biology Future Science Platform, CSIRO, Ringgold 261802; , Canberra, Australia
                [3 ]Department of Biology and Center for Biodiversity and Conservation Research, The University of Mississippi; , University, Mississippi, USA
                [4 ]Department of Biological Sciences, Louisiana State University; , Baton Rouge, Louisiana, USA
                [5 ]Centre for Australian National Biodiversity Research (a joint venture between the Parks Australia and CSIRO), Ringgold 170489; , Canberra, Australia
                University of Porto; , Porto, Portugal
                Author notes
                Address correspondence to Hadi Nazem-Bokaee, hadi.nazembokaee@ 123456anu.edu.au

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-4246-1804
                https://orcid.org/0000-0003-0964-0031
                https://orcid.org/0000-0001-9295-9996
                Article
                spectrum03660-23 spectrum.03660-23
                10.1128/spectrum.03660-23
                11705836
                39651901
                b7cdb34d-c5ef-4642-a40d-6121f30a6d82
                © Crown copyright 2024.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 25 October 2023
                : 05 November 2024
                Page count
                supplementary-material: 7, authors: 4, Figures: 2, Tables: 3, Equations: 6, References: 71, Pages: 17, Words: 9970
                Funding
                Funded by: DISR | Commonwealth Scientific and Industrial Research Organisation (CSIRO);
                Award ID: OD-206013
                Award Recipient :
                Funded by: National Science Foundation (NSF);
                Award ID: DEB-1846376
                Award Recipient :
                Categories
                Research Article
                computational-biology, Computational Biology
                Custom metadata
                January 2025

                microalgae,systems biology,metabolic network modeling,sorbitol,symbiosis

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