Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Glucose metabolism in Pseudomonas aeruginosa is cyclic when producing Polyhydroxyalkanoates and Rhamnolipids

Pseudomonas aeruginosa is an important chassis for production of polyhydroxyalkanoates (PHA) and rhamnolipids (RHL). Advances in the understanding of the biosynthesis metabolism of these biocompounds are crucial for increasing yield. ¹³C-Metabolic Flux Ratio Analysis (¹³C-MFA) is a technique to estimate in vivo metabolic fluxes ratios. PHA and RHL are essentially non-growth associated products of biotechnological interest and both contain hydroxyalkanoates (HAs), whose labeling patterns could be accessed by GC-MS. In this study, to reveal the relative contributions of the Entner-Doudoroff (ED) pathway and the non-oxidative Pentose Phosphate (PP) pathway to PHA and RHL production, ¹³C-MFA was performed in Pseudomonas aeruginosa LFM634 when supplied with labeled glucose. This bacterial strain lacks both functional EMP and the oxidative PP branch. Labeling patterns in HAs were measured. Experiments with [U-¹³C] glucose indicated a low flux though PP pathway. An optimal design of labeling experiment showed that [6-¹³C] glucose would be the best substrate to enable an estimation of the ED flux with high accuracy. Results of experiments performed with this isotope indicated that about two-thirds of glyceraldehyde 3-phosphate is recycled through a cyclic ED architecture, suggesting that P. aeruginosa utilizes that cycle to regulate the NADPH/Acetyl-CoA ratio for PHA and RHL biosynthesis.

Contextual Flexibility in Pseudomonas aeruginosa Central Carbon Metabolism during Growth in Single Carbon Sources

Pseudomonas aeruginosa is an opportunistic human pathogen that is well known for causing infections in the airways of people with cystic fibrosis. Although it is clear that P. aeruginosa is metabolically well adapted to life in the CF lung, little is currently known about how the organism metabolizes the nutrients available in the airways. In this work, we used a combination of gene expression and isotope tracer (“fluxomic”) analyses to find out exactly where the input carbon goes during growth on two CF-relevant carbon sources, acetate and glycerol (derived from the breakdown of lung surfactant). We found that carbon is routed (“fluxed”) through very different pathways during growth on these substrates and that this is accompanied by an unexpected remodeling of the cell’s electron transfer pathways. Having access to this “blueprint” is important because the metabolism of P. aeruginosa is increasingly being recognized as a target for the development of much-needed antimicrobial agents.

Pseudomonas aeruginosa is an opportunistic human pathogen, particularly noted for causing infections in the lungs of people with cystic fibrosis (CF). Previous studies have shown that the gene expression profile of P. aeruginosa appears to converge toward a common metabolic program as the organism adapts to the CF airway environment. However, we still have only a limited understanding of how these transcriptional changes impact metabolic flux at the systems level. To address this, we analyzed the transcriptome, proteome, and fluxome of P. aeruginosa grown on glycerol or acetate. These carbon sources were chosen because they are the primary breakdown products of an airway surfactant, phosphatidylcholine, which is known to be a major carbon source for P. aeruginosa in CF airways. We show that the fluxes of carbon throughout central metabolism are radically different among carbon sources. For example, the newly recognized “EDEMP cycle” (which incorporates elements of the Entner-Doudoroff [ED] pathway, the Embden-Meyerhof-Parnas [EMP] pathway, and the pentose phosphate [PP] pathway) plays an important role in supplying NADPH during growth on glycerol. In contrast, the EDEMP cycle is attenuated during growth on acetate, and instead, NADPH is primarily supplied by the reaction catalyzed by isocitrate dehydrogenase(s). Perhaps more importantly, our proteomic and transcriptomic analyses revealed a global remodeling of gene expression during growth on the different carbon sources, with unanticipated impacts on aerobic denitrification, electron transport chain architecture, and the redox economy of the cell. Collectively, these data highlight the remarkable metabolic plasticity of P. aeruginosa; that plasticity allows the organism to seamlessly segue between different carbon sources, maximizing the energetic yield from each.

In Vivo Proteome of Pseudomonas aeruginosa in Airways of Cystic Fibrosis Patients

Chronic airway infection with P. aeruginosa (PA) is a hallmark of cystic fibrosis (CF) disease. The mechanisms producing PA persistence in CF therapies remain poorly understood. To gain insight on PA physiology in patient airways and better understand how in vivo bacterial functioning differs from in vitro conditions, we investigated the in vivo proteomes of PA in 35 sputum samples from 11 CF patients. We developed a novel bacterial-enrichment method that relies on differential centrifugation and detergent treatment to enrich for bacteria to improve identification of PA proteome with CF sputum samples. Using two nonredundant peptides as a cutoff, a total of 1304 PA proteins were identified directly from CF sputum samples. The in vivo PA proteomes were compared with the proteomes of ex vivo-grown PA populations from the same patient sample. Label-free quantitation and proteome comparison revealed the in vivo up-regulation of siderophore TonB-dependent receptors, remodeling in central carbon metabolism including glyoxylate cycle and lactate utilization, and alginate overproduction. Knowledge of these in vivo proteome differences or others derived using the presented methodology could lead to future treatment strategies aimed at altering PA physiology in vivo to compromise infectivity or improve antibiotic efficacy.

Bacterial Metabolism During Biofilm Growth Investigated by 13C Tracing

This study investigated the metabolism of Pseudomonas aeruginosa PAO1 during its biofilm development via microscopy imaging, gene expression analysis, and ¹³C-labeling. First, dynamic labeling was employed to investigate glucose utilization rate in fresh biofilms (thickness 40∼60 micrometer). The labeling turnover time of glucose-6-P indicated biofilm metabolism was substantially slower than planktonic cells. Second, PAO1 was cultured in continuous tubular biofilm reactors or shake flasks. Then ¹³C-metabolic flux analysis of PAO1 was performed based on the isotopomer patterns of proteinogenic amino acids. The results showed that PAO1 biofilm cells during growth conserved the flux features as their planktonic mode. (1) Glucose could be degraded by two cyclic routes (the TCA cycle and the Entner-Doudoroff-Embden-Meyerhof-Parnas loop) that facilitated NAD(P)H supplies. (2) Anaplerotic pathways (including pyruvate shunt) increased flux plasticity. (3) Biofilm growth phenotype did not require significant intracellular flux rewiring (variations between biofilm and planktonic flux network, normalized by glucose uptake rate as 100%, were less than 20%). (4) Transcription analysis indicated that key catabolic genes in fresh biofilm cells had expression levels comparable to planktonic cells. Finally, PAO1, Shewanella oneidensis (as the comparing group), and their c-di-GMP transconjugants (with different biofilm formation capabilities) were ¹³C-labeled under biofilm reactors or planktonic conditions. Analysis of amino acid labeling variances from different cultures indicated Shewanella flux network was more flexibly changed than PAO1 during its biofilm formation.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.