<i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of <i>Spirulina</i> under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in...

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Main Authors: Supatcha Lertampaiporn, Jittisak Senachak, Wassana Taenkaew, Chiraphan Khannapho, Apiradee Hongsthong
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Cells
Subjects:
Online Access:https://www.mdpi.com/2073-4409/9/9/2097
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spelling doaj-38e4d9224abc4466a00e50d6709b36912020-11-25T03:06:47ZengMDPI AGCells2073-44092020-09-0192097209710.3390/cells9092097<i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance AnalysisSupatcha Lertampaiporn0Jittisak Senachak1Wassana Taenkaew2Chiraphan Khannapho3Apiradee Hongsthong4Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, 49 Soi Thian Thale 25, Tha Kham, Bang Khun Thian, Bangkok 10150, ThailandBiochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, 49 Soi Thian Thale 25, Tha Kham, Bang Khun Thian, Bangkok 10150, ThailandPilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi, 49 Soi Thian Thale 25, Tha Kham, Bang Khun Thian, Bangkok 10150, ThailandBiochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, 49 Soi Thian Thale 25, Tha Kham, Bang Khun Thian, Bangkok 10150, ThailandBiochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, 49 Soi Thian Thale 25, Tha Kham, Bang Khun Thian, Bangkok 10150, ThailandThis study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of <i>Spirulina</i> under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of <i>Spirulina</i> to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The <i>Spirulina</i>, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.https://www.mdpi.com/2073-4409/9/9/2097genome-scaleflux balance analysisproteome analysistemperature responsehistidine kinasein silico mutation
collection DOAJ
language English
format Article
sources DOAJ
author Supatcha Lertampaiporn
Jittisak Senachak
Wassana Taenkaew
Chiraphan Khannapho
Apiradee Hongsthong
spellingShingle Supatcha Lertampaiporn
Jittisak Senachak
Wassana Taenkaew
Chiraphan Khannapho
Apiradee Hongsthong
<i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
Cells
genome-scale
flux balance analysis
proteome analysis
temperature response
histidine kinase
in silico mutation
author_facet Supatcha Lertampaiporn
Jittisak Senachak
Wassana Taenkaew
Chiraphan Khannapho
Apiradee Hongsthong
author_sort Supatcha Lertampaiporn
title <i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
title_short <i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
title_full <i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
title_fullStr <i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
title_full_unstemmed <i>Spirulina</i>-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis
title_sort <i>spirulina</i>-in silico-mutations and their comparative analyses in the metabolomics scale by using proteome-based flux balance analysis
publisher MDPI AG
series Cells
issn 2073-4409
publishDate 2020-09-01
description This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of <i>Spirulina</i> under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of <i>Spirulina</i> to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The <i>Spirulina</i>, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.
topic genome-scale
flux balance analysis
proteome analysis
temperature response
histidine kinase
in silico mutation
url https://www.mdpi.com/2073-4409/9/9/2097
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