Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection

Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino acids, and vitamins. In this study, we assembled and validated a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron (iAH991), a prominent representative of the human gut microbiota, consisting of 1488 reactions, 1152 metabolites, and 991 genes. To create a comprehensive metabolic model of host-microbe interactions, we integrated iAH991 with a previously published mouse metabolic reconstruction, which was extended for intestinal transport and absorption reactions. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes. The resulting model consists of 7239 reactions, 5164 metabolites, and 2769 genes. We simultaneously modeled growth of mouse and B. thetaiotaomicron on five different diets varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa. This systems approach represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease.

In silico systems biology approaches for the identification of antimicrobial targets

Classical antibiotic discovery efforts have relied mainly on molecular library screening coupled with target-based lead optimization. The conventional approaches are unable to tackle the emergence of antibiotic resistance and are failing to provide understanding of multiple mechanisms behind drug actions and the off-target effects. These insufficiencies have prompted researchers to focus on a multidisciplinary approach of systems biology-based antibiotic discovery. Systems biology is capable of providing a big-picture view for therapeutic targets through interconnected networks of biochemical reactions derived from both experimental and computational techniques. In this chapter, we have compiled software tools and databases that are typically used for target identification through in silico analyses. We have also identified enzyme- and broad-spectrum metabolite-based drug targets that have emerged through in silico systems microbiology.

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

Molecular epidemiology of chronic Pseudomonas aeruginosa airway infections in cystic fibrosis

Background/Methods

The molecular epidemiology of the chronic airway infections with Pseudomonas aeruginosa in individuals with cystic fibrosis (CF) was investigated by cross-sectional analysis of bacterial isolates from 51 CF centers and by longitudinal analysis of serial isolates which had been collected at the CF centers Hanover and Copenhagen since the onset of airway colonization over 30 years.

Results

Genotyping revealed that the P. aeruginosa population in CF is dominated by a few ubiquitous clones. The five most common clones retrieved from the CF host also belonged to the twenty most frequent clones in the environment and in other human disease habitats. Turnover of clones in CF airways was rare. At the Hanover clinic more than half of the patient cohort was still harbouring the initially acquired clone after twenty years of airway colonization. At the Copenhagen clinic, however, two rare clones replaced the initially acquired individual clones in all but one patient.

Conclusion

The divergent epidemiology at the two sites is explained by their differential management of hygiene and antipseudomonal chemotherapy. Hygienic measures to prohibit patient-to-patient transmission and the modalities of antipseudomonal chemotherapy modify the epidemiology of the chronic P. aeruginosa infections in CF.

A systems biology approach to studying the role of microbes in human health

Host-microbe interactions play a crucial role in human health and disease. Of the various systems biology approaches, reconstruction of genome-scale metabolic networks combined with constraint-based modeling has been particularly successful at in silico predicting the phenotypic characteristics of single organisms. Here, we summarize recent studies, which have applied this approach to investigate microbe-microbe and host-microbe metabolic interactions. This approach can be also expanded to investigate the properties of an entire microbial community, as well as single organisms within the community. We illustrate that the constraint-based modeling approach is suitable to model host-microbe interactions at molecular resolution and will enable systematic investigation of metabolic links between the human host and its microbes. Such host-microbe models, combined with experimental data, will ultimately further our understanding of how microbes influence human health.

Omics approaches in cystic fibrosis research: a focus on oxylipin profiling in airway secretions

Cystic fibrosis (CF) is associated with abnormal lipid metabolism, intense respiratory tract (RT) infection, and inflammation, eventually resulting in lung tissue destruction and respiratory failure. The CF RT inflammatory milieu, as reflected by airway secretions, includes a complex array of inflammatory mediators, bacterial products, and host secretions. It is dominated by neutrophils and their proteolytic and oxidative products and includes a wide spectrum of bioactive lipids produced by both host and presumably microbial metabolic pathways. The fairly recent advent of "omics" technologies has greatly increased capabilities of further interrogating this easily obtainable RT compartment that represents the apical culture media of the underlying RT epithelial cells. This paper discusses issues related to the study of CF omics with a focus on the profiling of CF RT oxylipins. Challenges in their identification/quantitation in RT fluids, their pathways of origin, and their potential utility for understanding CF RT inflammatory and oxidative processes are highlighted. Finally, the utility of oxylipin metabolic profiling in directing optimal therapeutic approaches and determining the efficacy of various interventions is discussed.

What mRNA Abundances Can Tell us about Metabolism

Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.

Integration of expression data in genome-scale metabolic network reconstructions

With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of "omics" data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA), a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm

Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial adaptability and antibiotic resistance. We employed a systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. A previously reconstructed metabolic model of Pseudomonas aeruginosa (PA) was used to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions. A set of 26 genes essential in both conditions was identified. Moreover, these genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be used to slow growth specifically in biofilm-associated PA. Furthermore, we performed a double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. While most of the difference in double essential gene sets could be explained by different medium composition found in biofilm-like and planktonic conditions, we observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions. These gene sets may serve as novel metabolic drug targets to combat particularly biofilm-associated PA. Taken together, this study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets.

Superessential reactions in metabolic networks

The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.

Genomic expression analysis reveals strategies of Burkholderia cenocepacia to adapt to cystic fibrosis patients' airways and antimicrobial therapy

Pulmonary colonization of cystic fibrosis (CF) patients with Burkholderia cenocepacia or other bacteria of the Burkholderia cepacia complex (Bcc) is associated with worse prognosis and increased risk of death. During colonization, the bacteria may evolve under the stressing selection pressures exerted in the CF lung, in particular, those resulting from challenges of the host immune defenses, antimicrobial therapy, nutrient availability and oxygen limitation. Understanding the adaptive mechanisms that promote successful colonization and long-term survival of B. cenocepacia in the CF lung is essential for an improved therapeutic outcome of chronic infections. To get mechanistic insights into these adaptive strategies a transcriptomic analysis, based on DNA microarrays, was explored in this study. The genomic expression levels in two clonal variants isolated during long-term colonization of a CF patient who died from the cepacia syndrome were compared. One of the isolates examined, IST439, is the first B. cenocepacia isolate retrieved from the patient and the other isolate, IST4113, was obtained three years later and is more resistant to different classes of antimicrobials. Approximately 1000 genes were found to be differently expressed in the two clonal variants reflecting a marked reprogramming of genomic expression. The up-regulated genes in IST4113 include those involved in translation, iron uptake (in particular, in ornibactin biosynthesis), efflux of drugs and in adhesion to epithelial lung tissue and to mucin. Alterations related with adaptation to the nutritional environment of the CF lung and to an oxygen-limited environment are also suggested to be a key feature of transcriptional reprogramming occurring during long-term colonization, antibiotic therapy and the progression of the disease.

Individualized therapy of HHT driven by network analysis of metabolomic profiles

Background

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant disease with a varying range of phenotypes involving abnormal vasculature primarily manifested as arteriovenous malformations in various organs, including the nose, brain, liver, and lungs. The varied presentation and involvement of different organ systems makes the choice of potential treatment medications difficult.

Results

A patient with a mixed-clinical presentation and presumed diagnosis of HHT, severe exertional dyspnea, and diffuse pulmonary shunting at the microscopic level presented for treatment. We sought to analyze her metabolomic plasma profile to assist with pharmacologic treatment selection. Fasting serum samples from 5 individuals (4 healthy and 1 with HHT) were metabolomically profiled.

A global metabolic network reconstruction, Recon 1, was used to help guide the choice of medication via analysis of the differential metabolism between the patient and healthy controls using metabolomic data. Flux Balance Analysis highlighted changes in metabolic pathway activity, notably in nitric oxide synthase (NOS), which suggested a potential link between changes in vascular endothelial function and metabolism. This finding supported the use of an already approved medication, bevacizumab (Avastin). Following 2 months of treatment, the patient's metabolic profile shifted, becoming more similar to the control subject profiles, suggesting that the treatment was addressing at least part of the pathophysiological state.

Conclusions

In this 'individualized case study' of personalized medicine, we carry out untargeted metabolomic profiling of a patient and healthy controls. Rather than filtering the data down to a single value, these data are analyzed in the context of a network model of metabolism, in order to simulate the biochemical phenotypic differences between healthy and disease states; the results then guide the therapy. This presents one approach to achieving the goals of individualized medicine through Systems Biology and causal models analysis.

TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

Background

Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.

Results

We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae.

Conclusion

The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

In-vivo expression profiling of Pseudomonas aeruginosa infections reveals niche-specific and strain-independent transcriptional programs

Pseudomonas aeruginosa is a threatening, opportunistic pathogen causing disease in immunocompromised individuals. The hallmark of P. aeruginosa virulence is its multi-factorial and combinatorial nature. It renders such bacteria infectious for many organisms and it is often resistant to antibiotics. To gain insights into the physiology of P. aeruginosa during infection, we assessed the transcriptional programs of three different P. aeruginosa strains directly after isolation from burn wounds of humans. We compared the programs to those of the same strains using two infection models: a plant model, which consisted of the infection of the midrib of lettuce leaves, and a murine tumor model, which was obtained by infection of mice with an induced tumor in the abdomen. All control conditions of P. aeruginosa cells growing in suspension and as a biofilm were added to the analysis. We found that these different P. aeruginosa strains express a pool of distinct genetic traits that are activated under particular infection conditions regardless of their genetic variability. The knowledge herein generated will advance our understanding of P. aeruginosa virulence and provide valuable cues for the definition of prospective targets to develop novel intervention strategies.

Genetic and Functional Diversity of Pseudomonas aeruginosa Lipopolysaccharide

Lipopolysccharide (LPS) is an integral component of the Pseudomonas aeruginosa cell envelope, occupying the outer leaflet of the outer membrane in this Gram-negative opportunistic pathogen. It is important for bacterium-host interactions and has been shown to be a major virulence factor for this organism. Structurally, P. aeruginosa LPS is composed of three domains, namely, lipid A, core oligosaccharide, and the distal O antigen (O-Ag). Most P. aeruginosa strains produce two distinct forms of O-Ag, one a homopolymer of D-rhamnose that is a common polysaccharide antigen (CPA, formerly termed A band), and the other a heteropolymer of three to five distinct (and often unique dideoxy) sugars in its repeat units, known as O-specific antigen (OSA, formerly termed B band). Compositional differences in the O units among the OSA from different strains form the basis of the International Antigenic Typing Scheme for classification via serotyping of different strains of P. aeruginosa. The focus of this review is to provide state-of-the-art knowledge on the genetic and resultant functional diversity of LPS produced by P. aeruginosa. The underlying factors contributing to this diversity will be thoroughly discussed and presented in the context of its contributions to host-pathogen interactions and the control/prevention of infection.

Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis

In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species.

Microevolution of Pseudomonas aeruginosa to a chronic pathogen of the cystic fibrosis lung

Pseudomonas aeruginosa is the leading pathogen of chronic cystic fibrosis (CF) lung infection. Life-long persistance of P. aeruginosa in the CF lung requires a sophisticated habitat-specific adaptation of this pathogen to the heterogeneous and fluctuating lung environment. Due to the high selective pressure of inflamed CF lungs, P. aeruginosa increasingly experiences complex physiological and morphological changes. Pulmonary adaptation of P. aeruginosa is mediated by genetic variations that are fixed by the repeating interplay of mutation and selection. In this context, the emergence of hypermutable phenotypes (mutator strains) obviously improves the microevolution of P. aeruginosa to the diverse microenvironments of the CF lung. Mutator phenotypes are amplified during CF lung disease and accelerate the intraclonal diversification of P. aeruginosa. The resulting generation of numerous subclonal variants is advantegous to prepare P. aeruginosa population for unpredictable stresses (insurance hypothesis) and thus supports long-term survival of this pathogen. Oxygen restriction within CF lung environment further promotes persistence of P. aeruginosa due to increased antibiotic tolerance, alginate production and biofilm formation. Finally, P. aeruginosa shifts from an acute virulent pathogen of early infection to a host-adapted chronic virulent pathogen of end-stage infection of the CF lung. Common changes that are observed among chronic P. aeruginosa CF isolates include alterations in surface antigens, loss of virulence-associated traits, increasing antibiotic resistances, the overproduction of the exopolysaccharide alginate and the modulation of intermediary and micro-aerobic metabolic pathways (Hogardt and Heesemann, Int J Med Microbiol 300(8):557-562, 2010). Loss-of-function mutations in mucA and lasR genes determine the transition to mucoidity and loss of quorum sensing, which are hallmarks of the chronic virulence potential of P. aeruginosa. Metabolic factors that are positively selected in response to the specific environment of CF lung include the outer membrane protein OprF, the microaerophilic oxidase Cbb3-2, the blue copper protein azurin, the cytochrome c peroxidase c551 and the enzymes of the arginine deiminase pathway ArcA-ArcD. These metabolic adaptations probably support the growth of P. aeruginosa within oxygen-depleted CF mucus. The deeper understanding of the physiological mechanisms of niche specialization of P. aeruginosa during CF lung infection will help to identify new targets for future anti-pseudomonal treatment strategies to prevent the selection of mutator isolates and the establishment of chronic CF lung infection.

Whole-genome metabolic network reconstruction and constraint-based modeling

With the advent of modern high-throughput genomics, there is a significant need for genome-scale analysis techniques that can assist in complex systems analysis. Metabolic genome-scale network reconstructions (GENREs) paired with constraint-based modeling are an efficient method to integrate genomics, transcriptomics, and proteomics to conduct organism-specific analysis. This text explains key steps in the GENRE construction process and several methods of constraint-based modeling that can help elucidate basic life processes and development of disease treatment, bioenergy solutions, and industrial bioproduction applications.

Clinical significance of microbial infection and adaptation in cystic fibrosis

A select group of microorganisms inhabit the airways of individuals with cystic fibrosis. Once established within the pulmonary environment in these patients, many of these microbes adapt by altering aspects of their structure and physiology. Some of these microbes and adaptations are associated with more rapid deterioration in lung function and overall clinical status, whereas others appear to have little effect. Here we review current evidence supporting or refuting a role for the different microbes and their adaptations in contributing to poor clinical outcomes in cystic fibrosis.