Background: Enterobacteriaceae diversified from an ancestral lineage ~300-500 million years ago (mya) into a wide variety of free-living and host-associated lifestyles. Nutrient availability varies across niches, and evolution of metabolic networks likely played a key role in adaptation.Results: Here we use a paleo systems biology approach to reconstruct and model metabolic networks of ancestral nodes of the enterobacteria phylogeny to investigate metabolism of ancient microorganisms and evolution of the networks. Specifically, we identified orthologous genes across genomes of 72 free-living enterobacteria (16 genera), and constructed core metabolic networks capturing conserved components for ancestral lineages leading to E. coli/Shigella (~10 mya), E. coli/Shigella/Salmonella (~100 mya), and all enterobacteria (~300-500 mya). Using these models we analyzed the capacity for carbon, nitrogen, phosphorous, sulfur, and iron utilization in aerobic and anaerobic conditions, identified conserved and differentiating catabolic phenotypes, and validated predictions by comparison to experimental data from extant organisms.Conclusions: This is a novel approach using quantitative ancestral models to study metabolic network evolution and may be useful for identification of new targets to control infectious diseases caused by enterobacteria.
Bibliographical noteFunding Information:
This work was funded by the National Library of Medicine, National Institutes of Health, Grant No. 5T15LM007359 to the Computation and Informatics in Biology and Medicine Training Program for a Post-Doctoral Traineeship (D.J.B) and a graduate student fellowship (B.M). This work was also supported by an NSF Grant No.DEB-0936214 to NTP. Finally, we would also like to thank Bryan Biehl and Dr(s). Jeremy Glasner, Guy Plunkett III, and Eric Neeno-Eckwall for insightful discussions of the manuscript, and Dr. Eric Cabot and Christopher Tervo for assistance in generation of the SBML model files.
- Ancestral core
- Ancient metabolism
- Constraint-based modeling
- Metabolic network reconstruction
- Paleo systems biology