Cited by 24Microbial Metabolic Engineering and BioproductionAlgal biology and biofuel productionGene Regulatory Network Analysis

An Improved Genome-Scale Metabolic Model of Arthrospira platensis C1 (iAK888) and Its Application in Glycogen Overproduction

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Klanchui, Amornpan, Dulsawat, Sudarat, Chaloemngam, Kullapat, Cheevadhanarak, Supapon, Prommeenate, Peerada, Meechai, Asawin·2018·Metabolites

Abstract

Glycogen-enriched biomass of Arthrospira platensis has increasingly gained attention as a source for bioethanol production. To study the metabolic capabilities of glycogen production in A. platensis C1, a genome-scale metabolic model (GEM) could be a useful tool for predicting cellular behavior and suggesting strategies for glycogen overproduction. New experimentally validated GEM of A. platensis C1 namely iAK888, which has improved metabolic coverage and functionality was employed in this research. The iAK888 is a fully functional compartmentalized GEM consisting of 888 genes, 1,096 reactions, and 994 metabolites. This model was demonstrated to reasonably predict growth and glycogen fluxes under different growth conditions. In addition, iAK888 was further employed to predict the effect of deficiencies of NO3−, PO43−, or SO42− on the growth and glycogen production in A. platensis C1. The simulation results showed that these nutrient limitations led to a decrease in growth flux and an increase in glycogen flux. The experiment of A. platensis C1 confirmed the enhancement of glycogen fluxes after the cells being transferred from normal Zarrouk’s medium to either NO3−, PO43−, or SO42−-free Zarrouk’s media. Therefore, iAK888 could be served as a predictive model for glycogen overproduction and a valuable multidisciplinary tool for further studies of this important academic and industrial organism.

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Crossref · 10.3390/metabo8040084

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  • Under phosphorous starvation, compared with other starvation scenarios, there was no significant difference in biomass production compared to control, while glycogen production increased, reaching up to 30% DW after 18 h.

    “In this case, comparing phosphorous starvation with the other scenarios, it did not present a significant difference in the biomass production compared to the control, while an increase in the production of glycogen was observed, reaching up to 30% DW of glycogen content after 18 h of culture.”

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  1. [50]Zarrouk C. Contribution à l’étude d’une cyanophycéeInfluence de divers’ facteurs physiques et chimiques sur la croissance et la photosynthèse de Spirulina maxima PhD Thesis Université de Paris Paris, France 1996 (In French)

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Authors
Klanchui, A., Dulsawat, S., Chaloemngam, K., Cheevadhanarak, S., Prommeenate, P., & Meechai, A.
Affiliations
  • King Mongkut's University of Technology Thonburi (TH)
  • National Science and Technology Development Agency (TH)
Corresponding author
  • Prommeenate, Peerada
  • Meechai, Asawin
Venue
Metabolites
Publisher
MDPI AG
Published
2018-11-26
Volume
8
Issue
4
Pages
84
ISSN
2218-1989 (ISSN-L)
OpenAlex
W2903302662
Cited by
24
References
54
Citations by year
2026: 2 · 2025: 2 · 2024: 5 · 2023: 1 · 2022: 3 · 2021: 7 · 2020: 4
FWCI
0.98
Type
journal article
Language
en
Open access
Locations
  • Metabolites (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Center for Genetic Engineering and Biotechnology (P-11-01089)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
Spirulina platensis, genome-scale metabolic models, bioethanol production, nutrient limitation, nitrogen deficiency, phosphorus deficiency, sulfate deficiency, cyanobacterial metabolism, metabolic flux prediction

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