High-throughput metabolomics for the design and validation of a diauxic shift model
Abstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-023-00274-9 |
| _version_ | 1797850003108003840 |
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| author | Daniel Brunnsåker Gabriel K. Reder Nikul K. Soni Otto I. Savolainen Alexander H. Gower Ievgeniia A. Tiukova Ross D. King |
| author_facet | Daniel Brunnsåker Gabriel K. Reder Nikul K. Soni Otto I. Savolainen Alexander H. Gower Ievgeniia A. Tiukova Ross D. King |
| author_sort | Daniel Brunnsåker |
| collection | DOAJ |
| description | Abstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput. |
| first_indexed | 2024-04-09T18:54:23Z |
| format | Article |
| id | doaj.art-eb73b3bbf0e24d7193ca096c1f48177c |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-04-09T18:54:23Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-eb73b3bbf0e24d7193ca096c1f48177c2023-04-09T11:20:05ZengNature Portfolionpj Systems Biology and Applications2056-71892023-04-01911910.1038/s41540-023-00274-9High-throughput metabolomics for the design and validation of a diauxic shift modelDaniel Brunnsåker0Gabriel K. Reder1Nikul K. Soni2Otto I. Savolainen3Alexander H. Gower4Ievgeniia A. Tiukova5Ross D. King6Department of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyAbstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.https://doi.org/10.1038/s41540-023-00274-9 |
| spellingShingle | Daniel Brunnsåker Gabriel K. Reder Nikul K. Soni Otto I. Savolainen Alexander H. Gower Ievgeniia A. Tiukova Ross D. King High-throughput metabolomics for the design and validation of a diauxic shift model npj Systems Biology and Applications |
| title | High-throughput metabolomics for the design and validation of a diauxic shift model |
| title_full | High-throughput metabolomics for the design and validation of a diauxic shift model |
| title_fullStr | High-throughput metabolomics for the design and validation of a diauxic shift model |
| title_full_unstemmed | High-throughput metabolomics for the design and validation of a diauxic shift model |
| title_short | High-throughput metabolomics for the design and validation of a diauxic shift model |
| title_sort | high throughput metabolomics for the design and validation of a diauxic shift model |
| url | https://doi.org/10.1038/s41540-023-00274-9 |
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