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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions
BACKGROUND: Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted resu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247156/ https://www.ncbi.nlm.nih.gov/pubmed/34193117 http://dx.doi.org/10.1186/s12934-021-01614-2 |
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author | Zhou, Jingru Zhuang, Yingping Xia, Jianye |
author_facet | Zhou, Jingru Zhuang, Yingping Xia, Jianye |
author_sort | Zhou, Jingru |
collection | PubMed |
description | BACKGROUND: Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. RESULTS: Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale [Formula: see text] values, predicting the differential expression of enzymes under different growth conditions. CONCLUSIONS: This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-021-01614-2. |
format | Online Article Text |
id | pubmed-8247156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82471562021-07-06 Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions Zhou, Jingru Zhuang, Yingping Xia, Jianye Microb Cell Fact Research BACKGROUND: Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. RESULTS: Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale [Formula: see text] values, predicting the differential expression of enzymes under different growth conditions. CONCLUSIONS: This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-021-01614-2. BioMed Central 2021-06-30 /pmc/articles/PMC8247156/ /pubmed/34193117 http://dx.doi.org/10.1186/s12934-021-01614-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Jingru Zhuang, Yingping Xia, Jianye Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title | Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title_full | Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title_fullStr | Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title_full_unstemmed | Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title_short | Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions |
title_sort | integration of enzyme constraints in a genome-scale metabolic model of aspergillus niger improves phenotype predictions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247156/ https://www.ncbi.nlm.nih.gov/pubmed/34193117 http://dx.doi.org/10.1186/s12934-021-01614-2 |
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