Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes
Constraint-based genome-scale models (GEMs) of microorganisms provide a powerful tool for predicting and analyzing microbial phenotypes as well as for understanding how these are affected by genetic and environmental perturbations. Recently, MATLAB and Python-based tools have been developed to incor...
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| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023003276 |
| _version_ | 1797384096497795072 |
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| author | Antonio Caivano Wouter van Winden Giuliano Dragone Solange I. Mussatto |
| author_facet | Antonio Caivano Wouter van Winden Giuliano Dragone Solange I. Mussatto |
| author_sort | Antonio Caivano |
| collection | DOAJ |
| description | Constraint-based genome-scale models (GEMs) of microorganisms provide a powerful tool for predicting and analyzing microbial phenotypes as well as for understanding how these are affected by genetic and environmental perturbations. Recently, MATLAB and Python-based tools have been developed to incorporate enzymatic constraints into GEMs. These constraints enhance phenotype predictions by accounting for the enzyme cost of catalyzed model´s reactions, thereby reducing the space of possible metabolic flux distributions. In this study, enzymatic constraints were added to an existing GEM of Clostridium ljungdahlii, a model acetogenic bacterium, by including its enzyme turnover numbers (kcats) and molecular masses, using the Python-based AutoPACMEN approach. When compared to the metabolic model iHN637, the enzyme cost-constrained model (ec_iHN637) obtained in our study showed an improved predictive ability of growth rate and product profile. The model ec_iHN637 was then employed to perform in silico metabolic engineering of C. ljungdahlii, by using the OptKnock computational framework to identify knockouts to enhance the production of desired fermentation products. The in silico metabolic engineering was geared towards increasing the production of fermentation products by C. ljungdahlii, with a focus on the utilization of synthesis gas and CO2. This resulted in different engineering strategies for overproduction of valuable metabolites under different feeding conditions, without redundant knockouts for different products. Importantly, the results of the in silico engineering results indicated that the mixotrophic growth of C. ljungdahlii is a promising approach to coupling improved cell growth and acetate and ethanol productivity with net CO2 fixation. |
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| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:30:34Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-d14e72095a354198a2db814b09a89df92023-12-21T07:32:08ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012146344646Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processesAntonio Caivano0Wouter van Winden1Giuliano Dragone2Solange I. Mussatto3Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800, Kongens Lyngby, DenmarkDSM-Firmenich Science & Research - Bioprocess Innovation, Rosalind Franklin Biotechnology Center, Alexander Fleminglaan 1, 2613 AX, Delft, the NetherlandsDepartment of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800, Kongens Lyngby, DenmarkDepartment of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800, Kongens Lyngby, Denmark; Corresponding author.Constraint-based genome-scale models (GEMs) of microorganisms provide a powerful tool for predicting and analyzing microbial phenotypes as well as for understanding how these are affected by genetic and environmental perturbations. Recently, MATLAB and Python-based tools have been developed to incorporate enzymatic constraints into GEMs. These constraints enhance phenotype predictions by accounting for the enzyme cost of catalyzed model´s reactions, thereby reducing the space of possible metabolic flux distributions. In this study, enzymatic constraints were added to an existing GEM of Clostridium ljungdahlii, a model acetogenic bacterium, by including its enzyme turnover numbers (kcats) and molecular masses, using the Python-based AutoPACMEN approach. When compared to the metabolic model iHN637, the enzyme cost-constrained model (ec_iHN637) obtained in our study showed an improved predictive ability of growth rate and product profile. The model ec_iHN637 was then employed to perform in silico metabolic engineering of C. ljungdahlii, by using the OptKnock computational framework to identify knockouts to enhance the production of desired fermentation products. The in silico metabolic engineering was geared towards increasing the production of fermentation products by C. ljungdahlii, with a focus on the utilization of synthesis gas and CO2. This resulted in different engineering strategies for overproduction of valuable metabolites under different feeding conditions, without redundant knockouts for different products. Importantly, the results of the in silico engineering results indicated that the mixotrophic growth of C. ljungdahlii is a promising approach to coupling improved cell growth and acetate and ethanol productivity with net CO2 fixation.http://www.sciencedirect.com/science/article/pii/S2001037023003276Genome-scale modelAcetogenic bacteriaCO2 fixationFlux balance analysisOptKnockMetabolic engineering |
| spellingShingle | Antonio Caivano Wouter van Winden Giuliano Dragone Solange I. Mussatto Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes Computational and Structural Biotechnology Journal Genome-scale model Acetogenic bacteria CO2 fixation Flux balance analysis OptKnock Metabolic engineering |
| title | Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes |
| title_full | Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes |
| title_fullStr | Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes |
| title_full_unstemmed | Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes |
| title_short | Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes |
| title_sort | enzyme constrained metabolic model and in silico metabolic engineering of clostridium ljungdahlii for the development of sustainable production processes |
| topic | Genome-scale model Acetogenic bacteria CO2 fixation Flux balance analysis OptKnock Metabolic engineering |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023003276 |
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