High-throughput metabolomics for the design and validation of a diauxic shift model

NPJ Systems Biology and Applications · 7 authors, 5 centres

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TL;DR

This study developed an automated, high-throughput workflow using untargeted intracellular metabolomics to study the diauxic shift in yeast. It found that this metabolomic approach can effectively characterize metabolic changes during the shift and generate hypotheses about the function of poorly characterized genes.

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This preclinical study in Saccharomyces cerevisiae investigated the use of automated cultivation and untargeted intracellular metabolomics for high-throughput functional characterization during the diauxic shift. The central result was that the metabolomic data successfully identified biochemical features of the phase-change, including changes in glyoxylate, glutathione, and proline metabolism, and provided initial insights into the roles of poorly characterized genes like YGR067C, RTS3, and Tda1. The data was also used to validate and refine a computational metabolic model. Limitations include the model's reliance on steady-state assumptions and the need for additional biological measurements to fully validate the hypotheses generated. The study concludes that untargeted metabolomics is well-suited for generating hypotheses about gene function and metabolic activity in a high-throughput manner.

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other

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biomedical research

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AI pipeline

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ORIGINAL PAPER

PMC10082077

High-throughput metabolomics for the design and validation of a diauxic shift model

NPJ Systems Biology and Applications

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ABSTRACT

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. Subject terms: Systems biology, Biotechnology

CITATION EXPORT

Daniel Brunnsåker, Gabriel K Reder, Nikul K Soni et al.. High-throughput metabolomics for the design and validation of a diauxic shift model. NPJ Systems Biology and Applications. (2023). https://doi.org/10.1038/s41540-023-00274-9

AUTHORS

7 · 5 CENTRES

Daniel Brunnsåker, Gabriel K Reder, Nikul K Soni, Otto I Savolainen, Alexander H Gower, Ievgeniia A Tiukova, Ross D King

1Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
2Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
3Division of Industrial Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
4Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
5Alan Turing Institute, London, UK