Inferring ancient metabolism using ancestral core metabolic models of enterobacteria

Baumler et al. BMC Systems Biology 2013, 7:46 http://www.biomedcentral.com/1752-0509/7/46 RESEARCH ARTICLE Open Access Inferring ancient metabolism using ancestral core metabolic models of enterobacteria David J Baumler1*, Bing Ma1,4, Jennifer L Reed2 and Nicole T Perna1,3 Abstract 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. Keywords: Constraint-based modeling, Enterobacteria, Metabolic network reconstruction, Ancient metabolism, Paleo systems biology, Ancestral core Background Initially named for a group of intestinal bacteria, members of the family Enterobacteriaceae are distributed worldwide and are found in soil, water, agronomic crops and produce, plants and trees, and in animals ranging from insects to humans. Pathogenic enterobacteria cause biomedically and agriculturally significant diseases, and historically have resulted in numerous pandemics, foodborne outbreaks, and nosocomial infections, arguably impacting human health more than any other microbial family. Enterobacteria have been extensively studied in the laboratory due to their importance to human health and as standard laboratory strains for molecular biology. The family includes 44 distinct genera and 176 named species [1], and there are over 150 complete or nearly complete genomes currently available for enterobacteria. Extensive comparative analysis between these genomes * Correspondence: 1 Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin, USA Full list of author information is available at the end of the article has revealed some of the genomic variations linked to host/niche specialization. The metabolic gene content of these genomes is complex, with each strain predicted to contain over 800 genes encoding metabolic enzymes and transporters. One method to investigate the complexity of genome-scale metabolic networks is through the construction of computational models. Computational modeling of bacterial metabolism offers a promising approach to predict strain-to-strain variation in metabolic capabilities and microbial strategies used in different environments, including host tissues. The number of available genome-scale metabolic models (GEMs) has grown in the last ten years to over 50 GEMs, and they capture the metabolic capabilities of numerous microbial taxa important to human health, biotechnology and bioengineering [2,3]. Systems biology combines computational and experimental approaches to study the complexity of biological networks at a systems level, where the cellular components and their interactions lead to complex cellular behaviors. Genome-scale biological networks have proven useful for interpreting high- © 2013 Baumler et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Baumler et al. BMC Systems Biology 2013, 7:46 http://www.biomedcentral.com/1752-0509/7/46 throughput data and generating computational models. Mathematical models are constructed from network reconstructions, and they include variables, parameters, and equations to describe the potential behavior of these networks. Starting with E. coli K-12 numerous types of genome-scale biological networks have been constructed including metabolic, regulatory, and transcriptional and translational machinery [4-9], and additional GEMs for additional enterobacteria have recently been constructed [4,10-15]. To date, GEMs of enterobacteria have been constructed for three standard laboratory E. coli strains [4,6-8,10], four pathogenic E. coli strains [4], one Salmonella strain [14,16], one Klebsiella strain [12], two Yersinia strains [10,13], and one insect endosymbiont, Buchnera [15]. These GEMs have been used to bioengineer strains for valuable end product formation [17-22], to conduct simulations to investigate metabolic processes during host-pathogen interactions [14], to identify differentiating metabolic properties between commensal and pathogenic E. coli strains [4], and to provide insight into the genome evolution of other enterobacteria [23-25]. In addition to strain-specific enterobacterial GEMs, recently 16 E. coli genomes were used to construct models from the combined genomic content of these E. coli strains, representing the intersection (ancestral core) and union (pangenome) and revealed new insight into the evolution of this species [4]. Members of the family Enterobacteriaceae diversified from a common ancestor ~300-500 million years ago (mya) into a wide variety of free-living and hostassociated lifestyles [26,27], yet based on conserved metabolic phenotypes of all modern enterobacteria, little is known about ancestral traits of metabolism beyond that they were able to catabolize glucose and grow in the presence or absence of oxygen [1]. Here the metabolism of ancient microorganisms has been investigated by identifying orthologous genes shared in the genomes of 72 free-living enterobacteria from 16 genera, and constructing metabolic networks representing the ancestral core at three phylogenetic points: the E. coli/Shigella ancestral core (~10 mya), the E. coli/ Shigella/Salmonella ancestral core (~100 mya), and the enterobacterial ancestral core (~300-500 mya). Using these metabolic models we have analyzed the metabolic capacity for carbon, nitrogen, phosphorous, sulfur, and iron utilization in aerobic and anaerobic conditions and have identified conserved and differentiating catabolic phenotypes and validated these predictions by compariso (...truncated)