High-throughput metabolomics for the design and validation of a diauxic shift model
Artikel i vetenskaplig tidskrift, 2023

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.

Författare

Daniel Brunnsåker

Chalmers, Life sciences, Systembiologi

Gabriel Reder

Chalmers, Life sciences, Systembiologi

Nikulkumar Soni

Chalmers, Life sciences, Systembiologi

Otto Savolainen

Itä-Suomen Yliopisto

Chalmers, Life sciences, Systembiologi

Alexander Gower

Chalmers, Life sciences, Systembiologi

Ievgeniia Tiukova

Kungliga Tekniska Högskolan (KTH)

Chalmers, Life sciences, Systembiologi

Ross King

Alan Turing Institute

Chalmers, Life sciences, Systembiologi

University of Cambridge

NPJ systems biology and applications

20567189 (eISSN)

Vol. 9 1 11-

Ämneskategorier

Biokemi och molekylärbiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Genetik

DOI

10.1038/s41540-023-00274-9

PubMed

37029131

Relaterade dataset

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

DOI: 10.5281/zenodo.7105588

Mer information

Senast uppdaterat

2023-09-21