We present iMO1056, a genome-scale reconstruction of P. aeruginosa PAO1 that accounts for 1,056 genes encoding 1,030 proteins that are involved in 883 reactions. Specifically reconstructed pathways included those necessary for growth and for production of common virulence factors, including alginate, rhamnolipids, phenazines, and quorum-sensing molecules. iMO1056 was validated with experimental growth rate data from literature, Biolog viability data on multiple carbon sources, and a genome-scale gene essentiality set derived from two independent transposon mutant studies of P. aeruginosa. Model predictions matched well with experimental data in many cases.
Building a genome-scale reconstruction of PAO1 offered insights that would have been difficult to obtain through any other means, including evidence for annotation refinements, the ability to quantitatively predict growth yields and secretion rates under various conditions, and a potential way to probe metabolic stresses incurred by the production of various virulence factors that may be explored in future studies. During the model-building process, we were forced to approach each metabolic pathway quantitatively and comprehensively, so previous gaps in knowledge were highlighted and investigated in a rational manner. This process of gap analysis involves coupling in silico growth simulations with bioinformatic searches and literature mining to fill holes in otherwise known pathways and offers a unique tool for identifying areas of metabolism that require further elucidation. Through gap analysis of P. aeruginosa metabolism, we were able to refine the annotation of a number of genes with respect to their function, directionality, or stoichiometry. Some of these changes are highlighted in Table . These annotation refinements represent many crucial metabolic functions of P. aeruginosa, and without a systematic network-level approach to guide our analysis, it would have been difficult to highlight these weak points in current knowledge.
As in any genome-scale annotation effort, the network reconstruction process for P. aeruginosa
will require continued work in the future. Mismatches between in vivo and in silico essential genes have highlighted metabolic regions of uncertainty in current knowledge, and these discrepancies still require more work to be explained adequately. In addition, several pathways in the model remain incomplete due to a lack of available knowledge. These gaps include synthesis of cofactors (e.g., cobalamin and thiamine) and, notably, the entire fatty acid β-oxidation pathway, which exists in but has not been characterized extensively for pseudomonads, to our knowledge (52
). The Biolog data also indicated some pathways that should be investigated further in the future, such as substrates that PAO1 utilized but which are not in iMO1056 or for which iMO1056 has no membrane transporter. Furthermore, some pathways were not included in the model despite having been studied in the literature, simply because they were peripheral to the main processes of interest in this study (growth and expression of virulence traits). Thus, for instance, pathways for degrading aromatic compounds and for handling xenobiotics were generally not included in the model, and these offer areas for model expansion in the future. Specific validation involving controlled growth experiments with P. aeruginosa
will also be informative, and coupling wet lab and in silico experiments will lend greater insight into both specific functions and global properties of P. aeruginosa
Additionally, a genome-scale reconstruction of P. aeruginosa
metabolism enables the interrogation of several otherwise difficult research questions. For instance, it would be informative to compare the P. aeruginosa
metabolic network with metabolic networks of other related but nonpathogenic species. This comparison would allow probing of properties such as pathway redundancy and growth burden of key virulence pathways and would offer insight into how these system-level properties might affect pathogenicity. Such a genome-scale network analysis between a pathogen and a nonpathogen has never been done, to our knowledge, and could provide significant insight into the mechanisms for disease and possible therapeutic targets. Another question of great interest involves the enumeration of selective pressures on P. aeruginosa
in the CF lung environment. For example, P. aeruginosa
samples obtained from sputum during the life of a CF patient have shown gene mutations and significant alterations in gene expression over time (4
). Notably, convergent evolution toward a loss of virulence factors by P. aeruginosa
strains taken from multiple CF patients has been demonstrated, suggesting that virulence traits might be selected against once a stable infection has been achieved (60
). The effect of the loss of the lasR
gene, a master regulator of hundreds of virulence-related genes through the quorum-sensing system in P. aeruginosa
, has also been evaluated (8
). It was shown that loss of the lasR
gene conferred a growth advantage both to strains extracted from the lungs of CF patients and from strains that emerged by selection on rich medium, indicating that optimization of yield could act as a metabolic objective even in the CF lung (8
). This result is counterintuitive, as much of the success of P. aeruginosa
in chronic lung infections seems to lie in its ability to adopt a slow-growth phenotype, which would suggest that optimization of yield is perhaps not a strong selective pressure in that environment (6
). Regardless of whether slow-growing mutants of P. aeruginosa
in the CF lung environment are actually optimized for yield, these studies indicate that metabolic selective pressure might be a factor in the evolution of chronic CF strains. With the integration of simple regulatory rules into iMO1056, it will be possible to model different hypotheses about selective pressures in the lung and to analyze the causes for these selective processes.
In addition to the pathogenicity analysis and evolutionary studies outlined above, iMO1056 can serve as a valuable tool in interpreting and informing genome-scale transcriptomic studies of PAO1. Microarray analysis has attracted sizeable interest in the P. aeruginosa
community and was recently used to determine genome-level expression changes under various stresses and conditions (13
). Enabling a survey of system-level traits of an organism in a relatively unbiased way, microarrays are a crucial element in postgenomic analyses of cell phenotype. While gene expression data cannot be linked directly with metabolic fluxes, past studies have used gene expression data to indicate the regulation of whole pathways in metabolism, thus indicating global phenotypes (9
). Furthermore, metabolic reconstructions have been used to predict which genes in a network are likely to be coregulated, and overlaying expression data on a metabolic reconstruction can inform interpretation of microarrays within the context of a genome-scale model (9
is an organism of much interest for its various roles as an opportunistic pathogen (60
). From chronic lifelong infections of the lungs of CF patients to acute, highly deadly infections of severe wounds in burn victims, the robustness and environmental diversity of P. aeruginosa
are testament to its remarkable natural metabolic agility. We chose to focus our reconstruction effort on P. aeruginosa
PAO1 since it is the most studied P. aeruginosa
strain and is also the best-characterized strain, but with minor modifications to iMO1056 the model can be tailored to describe similar strains, such as the more virulent P. aeruginosa
). A genome-scale metabolic model represents a potentially enormous tool for rational drug design and prediction of cell phenotypes and, in conjunction with regulatory information, can serve in modeling disease processes and engineering therapeutic responses. For P. aeruginosa
, a bacterium whose metabolic diversity is a major determinant of virulence, a metabolic network reconstruction will serve as an essential component in a multifaceted and effective response to disease.