Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection

Abstract

System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of Pseudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.The last decade has witnessed an explosion in both the quantity and the pace of biological discovery. High throughput methods have been developed and leveraged at an expanding rate, with the accumulation of high throughput data outstripping the capacity for analysis using conventional methods (16,21). To face these new challenges, systems-focused methods have come to the forefront of biological discovery, enabling a synergistic merging of network analysis with the existing reductionist paradigms that have fueled biology for the past halfcentury (25,40).One of the most pressing applications of systems analysis is unraveling the myriad factors that combine to form human disease. This ambitious goal has motivated a surge of interest in the collection and analysis of microarray data, which has emerged as a dominant technology for gathering genome-scale data due to its relatively low cost, ubiquity, ease, and increasingly high resolution and reproducibility (42). In particular, microarrays for gene expression profiling have been used in longitudinal studies of disease, as it enables a glimpse at the internal changes cells undergo as a disease progresses. While many such studies have been published, very little modeldriven analysis has been leveraged toward interpreting these data at the network level. There is a tremendous need for this next level of analysis, as a network approach promises a deeper mechanistic understanding of whole-cell phenotypes that will be crucial for determining better therapies in the future.With the increase in life span of cystic fibrosis (CF) patients over the last several decades, bacterial infections of the thickened mucus of the lung have become the primary disease burden that must be managed in these patients today (23). The peculiarities of the CF lung mucosal environment render it a ripe environment for growth of Pseudomonas aeruginosa in particular, a notorious opportunistic pathogen that chronically infects the lungs of nearly every CF patient by an early age (32). Due ...

Chronic pulmonary pseudomonal infection in patients with cystic fibrosis: A model for early phase symbiotic evolution

“…The concept of bacterial symbiosis in CF airway infection is strongly supported by the long-standing observation that pseudomonal auxotrophic strains can be found in 33-86% CF patients (Barth & Pitt, 1996;Haussler et al, 1999;Hoboth et al, 2009;Hogardt et al, 2007;Oberhardt et al, 2010;Taylor et al, 1992;Thomas et al, 2000). This metabolic divergence associated with CF Pa demonstrates that lifestyle changes are not limited to bacteria-to-bacteria interactions, but also bacteria-to-host associations.…”

“…Strains that arise from a founder clone in the same airway show phenotypic dissociation (2-6 phenotypes per specimen) and carry quorum-sensing lasR mutations, as well as noncytotoxic variants with lesions in T3SS (type three secretion system) ExoS and PopD. Most importantly, the accumulation of lossof-function mutations in DNA mismatch repair genes, such as mutS, mutL, and uvrD, has been recognized for its effect on niche-adapted Pa strains with relaxed DNA replication fidelity, and collectively labeled as ''anti-mutator deficient'' properties (Barth & Pitt, 1995a;Dacheux et al, 2000Dacheux et al, , 2001Feliziani et al, 2010;Furukawa et al, 2006;Heurlier et al, 2005;Hogardt et al, 2007;Irvin et al, 1981;Oberhardt et al, 2010;Rau et al, 2010;Roy-Burman et al, 2001;Smith et al, 2006;Thomas et al, 2000;Vendeville et al, 2005;Zierdt & Schmidt 1964). Recently, terms such as ''cheater cells'' and ''easy riders'' have been used to describe this functional heterogeneity and isogenic interdependency (Xavier et al, 2011).…”

Clinical Significance of Microbial Infection and Adaptation in Cystic Fibrosis

“…This attenuation of virulence may help the bacteria hide from the host immune response by eliminating factors detected by the host and by causing less tissue damage, which itself may stimulate an inflammatory response. Alternatively, strains not burdened with the production of multiple virulence determinants may be able to grow more rapidly and thus have a fitness advantage (428). A third theme is that many of the adaptive phenotypes are interrelated.…”

Comparative Metabolic Systems Analysis of Pathogenic Burkholderia

“…LB contains amino acids, glucose, and salts. SCFM contains lactate in addition to similar carbon sources to LB; in addition, the uptake rates of all nutrients present in SCFM are constrained to 10 mmol/ gDW/h to represent the reduced nutrient availability in the cystic fibrosis lung environment, as done elsewhere (36). In M9 minimal medium, a single carbon source is enabled for uptake at 10 mmol/gDW/h to represent tested compounds in in vitro Biolog growth screening (34).…”