Network-level architecture and the evolutionary potential of underground metabolism

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 32; pp. 11762 - 11767
• Main Authors: Notebaart, Richard A., Szappanos, Balázs, Kintses, Bálint, Pál, Ferenc, Györkei, Ádám, Bogos, Balázs, Lázár, Viktória, Spohn, Réka, Csörgő, Bálint, Wagner, Allon, Ruppin, Eytan, Pál, Csaba, Papp, Balázs
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 12.08.2014; National Acad Sciences
• Subjects: Adaptation, Physiological - genetics; Biochemistry; Bioengineering; Biological Evolution; Biological Sciences; Cellular metabolism; Computer Simulation; Datasets; E coli; Enzymes; Enzymes - genetics; Enzymes - metabolism; Escherichia coli; Escherichia coli K12 - genetics; Escherichia coli K12 - metabolism; Escherichia coli Proteins - genetics; Escherichia coli Proteins - metabolism; Evolution; Evolutionary biology; Genome, Bacterial; Genomics; Metabolic Networks and Pathways - genetics; Metabolism; Metabolites; Models, Biological; Nutrient utilization; Open reading frames; Phenotype; Phenotypes; phenotype microarray; molecular evolution; network evolution; evolutionary innovation; enzyme promiscuity
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1406102111

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli . Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.