Abstract

Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates. The composition of these MMNs has defined a new functional class of genes termed Network Efficiency Determinants (NEDs). These genes, whilst not essential, are very rarely eliminated in constructing an MMN, suggesting that it is difficult for metabolism to be re-routed to obviate the need for such genes. Moreover, the removal of NED genes from an MMN significantly reduces its global efficiency. Bioinformatic analyses of the NED genes have revealed that not only do these genes have more genetic interactions than the bulk of metabolic genes but their protein products also show more protein-protein interactions. In yeast, NED genes are predominantly single-copy and are highly conserved across evolutionarily distant organisms. These features confirm the importance of the NED genes to the metabolic network, including why they are so rarely excluded during minimisation.

The construction of minimal metabolic networks contributes to our understanding of metabolism and biotechnological processes. Here, Jansen et al. have defined a class of genes that must be retained to ensure cell viability and rapid growth.

Details

Title
Minimisation of metabolic networks defines a new functional class of genes
Author
Jansen, Giorgio 1   VIAFID ORCID Logo  ; Qi, Tanda 2   VIAFID ORCID Logo  ; Latora, Vito 3   VIAFID ORCID Logo  ; Amoutzias, Grigoris D. 4   VIAFID ORCID Logo  ; Delneri, Daniela 2   VIAFID ORCID Logo  ; Oliver, Stephen G. 5   VIAFID ORCID Logo  ; Nicosia, Giuseppe 1   VIAFID ORCID Logo 

 University of Cambridge, Department of Biochemistry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934); University of Catania, Department of Biomedical & Biotechnological Sciences, Catania, Italy (GRID:grid.8158.4) (ISNI:0000 0004 1757 1969) 
 University of Manchester, Manchester Institute of Biotechnology, Manchester, UK (GRID:grid.5379.8) (ISNI:0000 0001 2166 2407) 
 Queen Mary University of London, School of Mathematical Sciences, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); University of Catania, Department of Physics and I.N.F.N., Catania, Italy (GRID:grid.8158.4) (ISNI:0000 0004 1757 1969) 
 University of Thessaly, Bioinformatics Laboratory, Department of Biochemistry & Biotechnology, Thessaly, Greece (GRID:grid.410558.d) (ISNI:0000 0001 0035 6670) 
 University of Cambridge, Department of Biochemistry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934) 
Pages
9076
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3122902074
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.