Horizontal and vertical growth of S. cerevisiae metabolic network.

Luigi Grassi, Anna Tramontano

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

BACKGROUND: The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions. RESULTS: We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago. CONCLUSIONS: Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.
Original languageEnglish (US)
Pages (from-to)301
JournalBMC Evolutionary Biology
Volume11
Issue number1
DOIs
StatePublished - Nov 16 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-I1-012-43
Acknowledgements: This work was partially supported by Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST), by Fondazione Roma and by the Italian Ministry of Health, contract no.onc_ord 25/07, FIRB ITALBIONET and PROTEOMICA.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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