Comparative metabolic systems analysis of pathogenic Burkholderia

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts

Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.

Showing the limitations of available phenotypic assays to detect Burkholderia pseudomallei from clinical specimens in Nigeria

The genus Burkholderia comprises Gram-negative bacteria that are metabolically complex and versatile, often thriving in hostile settings. Burkholderia pseudomallei , the causative agent of melioidosis, is a prominent member of the genus and a clinical pathogen in tropical and sub-tropical regions. This pathogen is well known for its multidrug resistance and possible bioweapon potential. There is currently no report of the pathogen from clinical specimens in Nigeria, which might be due to misdiagnosis with phenotypic assays. This study aims to explore the accuracy of the use of phenotypic assays to diagnose B. pseudomallei in Nigeria. Two hundred and seventeen clinical samples and 28 Gram-negative clinical isolates were collected and analysed using Ashdown's selective agar and monoclonal antibody-based latex agglutination. Species-level identification was achieved using the analytical profile index (API) 20NE system. The susceptibility of the isolates to nine different antimicrobial agents was determined using the disc diffusion method. A total of seventy-four culture-positive isolates were obtained using Ashdown's selective agar. Twenty-two of these isolates were believed to be B. pseudomallei through the monoclonal antibody-based latex agglutination test and the API 20NE system subsequently identified 14 isolates as Burkholderia . The predominant Burkholderia species was B. cepacia with an isolation rate of 30.8 % (8/26). No isolate was distinctively identified as B. pseudomallei but five isolates were strongly suspected to be B. pseudomallei with similarity indices ranging from 81.9-91.3 %. Other bacterial species with definitive identity include Aeromonas sp., Sphingomonas sp. and Pseudomonas aeruginosa . The antibiotic susceptibility results revealed an overall resistance to amoxicillin-clavullanic acid of 71.4 %, to cefepime of 33.3 %, to trimethoprim-sulfamethoxazole of 38.1 %, to piperacillin-tazobactam of 33.3 %, to imipenem of 66.7 %, to doxycycline of 57.1% and to ceftazidime of 66.7 %. The highest intermediate resistance was observed for cefepime and piperacillin-tazobactam with a value of 66.7 % each, while there was no intermediate resistance for gentamicin, colistin and imipenem. Our findings, therefore, show that phenotypic assays alone are not sufficient in the diagnosis of melioidosis. Additionally, they provide robust support for present and future decisions to expand diagnostic capability for melioidosis beyond phenotypic assays in low-resource settings.

Surface Motility Favors Codependent Interaction between Pseudomonas aeruginosa and Burkholderia cenocepacia

Interactions between different bacterial species shape bacterial communities and their environments. The opportunistic pathogens Pseudomonas aeruginosa and Burkholderia cenocepacia both can colonize the lungs of individuals affected by cystic fibrosis. Using the social surface behavior called swarming motility as a study model, we noticed intricate interactions between B. cenocepacia K56-2 and P. aeruginosa PA14. While strain K56-2 does not swarm under P. aeruginosa favorable swarming conditions, co-inoculation with a nonmotile PA14 flagellum-less ΔfliC mutant restored spreading for both strains. We show that P. aeruginosa provides the wetting agent rhamnolipids allowing K56-2 to perform swarming motility, while aflagellated PA14 appears to “hitchhike” along with K56-2 cells in the swarming colony.

IMPORTANCEPseudomonas aeruginosa and Burkholderia cenocepacia are important opportunistic pathogens often found together in the airways of persons with cystic fibrosis. Laboratory cocultures of both species often ends with one taking over the other. We used a surface motility assay to study the social interactions between populations of these bacterial species. Under our conditions, B. cenocepacia cannot swarm without supplementation of the wetting agent produced by P. aeruginosa. In a mixed colony of both species, an aflagellated mutant of P. aeruginosa provides the necessary wetting agent to B. cenocepacia, allowing both bacteria to swarm and colonize a surface. We highlight this peculiar interaction where both bacteria set aside their antagonistic tendencies to travel together.

Genome-Scale Metabolic Model of Xanthomonas phaseoli pv. manihotis: An Approach to Elucidate Pathogenicity at the Metabolic Level

Xanthomonas phaseoli pv. manihotis (Xpm) is the causal agent of cassava bacterial blight, the most important bacterial disease in this crop. There is a paucity of knowledge about the metabolism of Xanthomonas and its relevance in the pathogenic process, with the exception of the elucidation of the xanthan biosynthesis route. Here we report the reconstruction of the genome-scale model of Xpm metabolism and the insights it provides into plant-pathogen interactions. The model, iXpm1556, displayed 1,556 reactions, 1,527 compounds, and 890 genes. Metabolic maps of central amino acid and carbohydrate metabolism, as well as xanthan biosynthesis of Xpm, were reconstructed using Escher (https://escher.github.io/) to guide the curation process and for further analyses. The model was constrained using the RNA-seq data of a mutant of Xpm for quorum sensing (QS), and these data were used to construct context-specific models (CSMs) of the metabolism of the two strains (wild type and QS mutant). The CSMs and flux balance analysis were used to get insights into pathogenicity, xanthan biosynthesis, and QS mechanisms. Between the CSMs, 653 reactions were shared; unique reactions belong to purine, pyrimidine, and amino acid metabolism. Alternative objective functions were used to demonstrate a trade-off between xanthan biosynthesis and growth and the re-allocation of resources in the process of biosynthesis. Important features altered by QS included carbohydrate metabolism, NAD(P)+ balance, and fatty acid elongation. In this work, we modeled the xanthan biosynthesis and the QS process and their impact on the metabolism of the bacterium. This model will be useful for researchers studying host-pathogen interactions and will provide insights into the mechanisms of infection used by this and other Xanthomonas species.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Small RNA NcS27 co-regulates utilization of carbon sources in Burkholderia cenocepacia J2315

Small non-coding sRNAs have versatile roles in regulating bacterial metabolism. Four short homologous Burkholderia cenocepacia sRNAs strongly expressed under conditions of growth arrest were recently identified. Here we report the detailed investigation of one of these, NcS27. sRNA NcS27 contains a short putative target recognition sequence, which is conserved throughout the order Burkholderiales. This sequence is the reverse complement of the Shine-Dalgarno sequence of a large number of genes involved in transport and metabolism of amino acids and carbohydrates. Overexpression of NcS27 sRNA had a distinct impact on growth, attenuating growth on a variety of substrates such as phenylalanine, tyrosine, glycerol and galactose, while having no effect on growth on other substrates. Transcriptomics and proteomics of NcS27 overexpression and silencing mutants revealed numerous predicted targets changing expression, notably of genes involved in degradation of aromatic amino acids phenylalanine and tyrosine, and in transport of carbohydrates. The conserved target recognition sequence was essential for growth phenotypes and gene expression changes. Cumulatively, our data point to a role of NcS27 in regulating the shutdown of metabolism upon nutrient deprivation in B. cenocepacia. We propose Burkholderiadouble-hairpin sRNA regulator bdhR1 as designation for ncS27.

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy.

IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Risk-Based Bioengineering Strategies for Reliable Bacterial Vaccine Production

Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design-Build-Test-Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability.

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.

Network Analyses in Plant Pathogens

Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production

BACKGROUND

Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.

METHODS

We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.

RESULTS

We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida.

CONCLUSIONS

We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.

Coupling Fluxes, Enzymes, and Regulation in Genome-Scale Metabolic Models

Genome-scale models have expanded beyond their metabolic origins. Multiple modeling frameworks are required to combine metabolism with enzymatic networks, transcription, translation, and regulation. Mathematical programming offers a powerful set of tools for tackling these "multi-modality" models, although special attention must be paid to the connections between modeling types. This chapter reviews common methods for combining metabolic and discrete logical models into a single mathematical programming framework. Best practices, caveats, and recommendations are presented for the most commonly used software packages. Methods for troubleshooting large sets of logical rules are also discussed.

The Essential Genome of Burkholderia cenocepacia H111

The study of the minimum set of genes required to sustain life is a fundamental question in biological research. Recent studies on bacterial essential genes suggested that between 350 and 700 genes are essential to support autonomous bacterial cell growth. Essential genes are of interest as potential new antimicrobial drug targets; hence, our aim was to identify the essential genome of the cystic fibrosis (CF) isolate Burkholderia cenocepacia H111. Using a transposon sequencing (Tn-Seq) approach, we identified essential genes required for growth in rich medium under aerobic and microoxic conditions as well as in a defined minimal medium with citrate as a sole carbon source. Our analysis suggests that 398 genes are required for autonomous growth in rich medium, a number that represents only around 5% of the predicted genes of this bacterium. Five hundred twenty-six genes were required to support growth in minimal medium, and 434 genes were essential under microoxic conditions (0.5% O₂). A comparison of these data sets identified 339 genes that represent the minimal set of essential genes required for growth under all conditions tested and can be considered the core essential genome of B. cenocepacia H111. The majority of essential genes were found to be located on chromosome 1, and few such genes were located on chromosome 2, where most of them were clustered in one region. This gene cluster is fully conserved in all Burkholderia species but is present on chromosome 1 in members of the closely related genus Ralstonia, suggesting that the transfer of these essential genes to chromosome 2 in a common ancestor contributed toward the separation of the two genera.IMPORTANCE Transposon sequencing (Tn-Seq) is a powerful method used to identify genes that are essential for autonomous growth under various conditions. In this study, we have identified a set of "core essential genes" that are required for growth under multiple conditions, and these genes represent potential antimicrobial targets. We also identified genes specifically required for growth under low-oxygen and nutrient-limited environments. We generated conditional mutants to verify the results of our Tn-Seq analysis and demonstrate that one of the identified genes was not essential per se but was an artifact of the construction of the mutant library. We also present verified examples of genes that were not truly essential but, when inactivated, showed a growth defect. These examples have identified so-far-underestimated shortcomings of this powerful method.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

Comparative genomics of Burkholderia multivorans, a ubiquitous pathogen with a highly conserved genomic structure

The natural environment serves as a reservoir of opportunistic pathogens. A well-established method for studying the epidemiology of such opportunists is multilocus sequence typing, which in many cases has defined strains predisposed to causing infection. Burkholderia multivorans is an important pathogen in people with cystic fibrosis (CF) and its epidemiology suggests that strains are acquired from non-human sources such as the natural environment. This raises the central question of whether the isolation source (CF or environment) or the multilocus sequence type (ST) of B. multivorans better predicts their genomic content and functionality. We identified four pairs of B. multivorans isolates, representing distinct STs and consisting of one CF and one environmental isolate each. All genomes were sequenced using the PacBio SMRT sequencing technology, which resulted in eight high-quality B. multivorans genome assemblies. The present study demonstrated that the genomic structure of the examined B. multivorans STs is highly conserved and that the B. multivorans genomic lineages are defined by their ST. Orthologous protein families were not uniformly distributed among chromosomes, with core orthologs being enriched on the primary chromosome and ST-specific orthologs being enriched on the second and third chromosome. The ST-specific orthologs were enriched in genes involved in defense mechanisms and secondary metabolism, corroborating the strain-specificity of these virulence characteristics. Finally, the same B. multivorans genomic lineages occur in both CF and environmental samples and on different continents, demonstrating their ubiquity and evolutionary persistence.

Identification and analysis of genomic islands in Burkholderia cenocepacia AU 1054 with emphasis on pathogenicity islands

BACKGROUND

Genomic islands (GIs) are genomic regions that reveal evidence of horizontal DNA transfer. They can code for many functions and may augment a bacterium's adaptation to its host or environment. GIs have been identified in strain J2315 of Burkholderia cenocepacia, whereas in strain AU 1054 there has been no published works on such regions according to our text mining and keyword search in Medline.

RESULTS

In this study, we identified 21 GIs in AU 1054 by combining two computational tools. Feature analyses suggested that the predictions are highly reliable and hence illustrated the advantage of joint predictions by two independent methods. Based on putative virulence factors, four GIs were further identified as pathogenicity islands (PAIs). Through experiments of gene deletion mutants in live bacteria, two putative PAIs were confirmed, and the virulence factors involved were identified as lipA and copR. The importance of the genes lipA (from PAI 1) and copR (from PAI 2) for bacterial invasion and replication indicates that they are required for the invasive properties of B. cenocepacia and may function as virulence determinants for bacterial pathogenesis and host infection.

CONCLUSIONS

This approach of in silico prediction of GIs and subsequent identification of potential virulence factors in the putative island regions with final validation using wet experiments could be used as an effective strategy to rapidly discover novel virulence factors in other bacterial species and strains.

Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

Metabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a system will behave, as well as to design systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which accounts for uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, this approach enables the modeling of biochemical systems which would otherwise be infeasible.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.

1H-NMR-Based Endometabolome Profiles of Burkholderia cenocepacia Clonal Variants Retrieved from a Cystic Fibrosis Patient during Chronic Infection

During cystic fibrosis (CF) chronic lung infections, bacteria of the Burkholderia cepacia complex (Bcc) are exposed for several years to a stressful and changing environment. These environmental challenges results in genetic changes of the initial infecting strain with the consequent diversification of genotypes and phenotypes. The exploitation of functional and comparative genomic approaches has suggested that such diversification is associated with massive metabolic remodeling but these alterations are poorly understood. In the present work, we have explored a high resolution ¹H-NMR-based metabolomic approach coupled to multivariate analysis to compare the endometabolome of three B. cenocepacia clonal variants retrieved from a CF patient from the onset of infection (IST439) until death with cepacia syndrome after 3.5 years (IST4113 and IST4134), to complement former proteomic and transcriptomic analyses. A fourth clonal variant (IST4129) retrieved from the same CF patient when the clinical condition worsened during the last months of life, was also examined since it was found to lack the third replicon. The metabolomic profiles obtained, based on the complete ¹H-NMR spectra, highlight the separation of the four clonal variants examined, the most distinct profile corresponding to IST4129. Results indicate a variable content of several amino acids in the different isolates examined and suggest that glycolysis and the glyoxylate shunt are favored in late variants. Moreover, the concentration of two metabolites with demonstrated cellular protective functions against stress, glycine-betaine and trehalose, is different in the different isolates examined. However, no clear correlation could be established between their content and stress tolerance. For example, IST4113, previously found to be the most resistant variant to antimicrobials of different classes, exhibits low levels of trehalose and glycine-betaine but the highest resistance to heat and oxidative stress. Also, IST4129, with a high level of glycine-betaine but lacking the third replicon, previously associated with stress resistance and virulence, exhibits the highest susceptibility to all the stresses tested. Taken together, results from this study provide insights into the metabolic diversification of B. cenocepacia clonal variants during long-term infection of the CF airways.

Burkholderia pseudomallei-derived miR-3473 enhances NF-κB via targeting TRAF3 and is associated with different inflammatory responses compared to Burkholderia thailandensis in murine macrophages

BACKGROUND

Burkholderia pseudomallei (Bp) is the causative agent of melioidosis, a kind of tropical disease. Burkholderia thailandensis (Bt), with a high sequence similarity to Bp, is thought to be an avirulent organism. Since there are numerous similarities between Bp and Bt, their differences in pathogenesis of host response and related mechanism are still undermined. In recent years, microRNAs have been researched in many diseases, but seldom involved in bacterial infection, bacteria-host interaction or explaining the differences between virulent and avirulent species.

RESULTS

We found that Bp and Bt had similar phenotypes in terms of intracellular replication, dissemination (reflected by multinucleated giant cell formation), TNF-α release and apoptosis in RAW264.7 macrophages or TC-1 pulmonary cell but in different level. Especially, at the late infection phases (after 12 h post infection), Bp showed faster intracellular growth, stronger cytotoxicity, and higher TNF-α release. After microRNA array analysis, we found some microRNAs were significantly expressed in macrophages treated by Bp. miR-3473 was one of them specifically induced, but not significantly changed in Bt-treated macrophages. In addition, TargetScan suggested that miR-3473 possibly target TRAF3 (TNF receptor-associated factor 3), a well-known negative regulator of the NF-κB pathway, which was probably involved in the TNF-α induction and apoptosis in cells with Bp infection. In vivo, it was found that miR-3473 expression of total lungs cells from Bp-treated was higher than that from Bt-treated mice. And miR-3473 inhibitor was able to decrease the TNF-α release of mice and prolong the survival of mice with Bp infection.

CONCLUSION

In sum, miR-3473 plays an important role in the differential pathogenicity of Bp and Bt via miR-3473-TRAF3-TNF-α network, and regulates TNF-α release, cell apoptosis and animal survival after Bp treatment. In this study, we have found a specific microRNA is related to bacterial virulence and provide insight into the mechanism for host-bacteria interaction, which suggests that potential oligonucleotides should be applied against bacterial infection.

Systems-level metabolism of the altered Schaedler flora, a complete gut microbiota

The altered Schaedler flora (ASF) is a model microbial community with both in vivo and in vitro relevance. Here we provide the first characterization of the ASF community in vitro, independent of a murine host. We compared the functional genetic content of the ASF to wild murine metagenomes and found that the ASF functionally represents wild microbiomes better than random consortia of similar taxonomic composition. We developed a chemically defined medium that supported growth of seven of the eight ASF members. To elucidate the metabolic capabilities of these ASF species—including potential for interactions such as cross-feeding—we performed a spent media screen and analyzed the results through dynamic growth measurements and non-targeted metabolic profiling. We found that cross-feeding is relatively rare (32 of 3570 possible cases), but is enriched between Clostridium ASF356 and Parabacteroides ASF519. We identified many cases of emergent metabolism (856 of 3570 possible cases). These data will inform efforts to understand ASF dynamics and spatial distribution in vivo, to design pre- and probiotics that modulate relative abundances of ASF members, and will be essential for validating computational models of ASF metabolism. Well-characterized, experimentally tractable microbial communities enable research that can translate into more effective microbiome-targeted therapies to improve human health.

Biosurfactants and surfactants interacting with membranes and proteins: Same but different?

Biosurfactants (BS) are surface-active molecules produced by microorganisms. For several decades they have attracted interest as promising alternatives to current petroleum-based surfactants. Aside from their green profile, they have remarkably low critical micelle concentrations, reduce the air/water surface tension to very low levels and are excellent emulsifiers, all of which make them comparable or superior to their synthetic counterparts. These remarkable physical properties derive from their more complex chemical structures in which hydrophilic and hydrophobic regions are not as clearly separated as chemical surfactants but have a more mosaic distribution of polarity as well as branched or circular structures. This allows the lipopeptide surfactin to adopt spherical structures to facilitate dense packing at interfaces. They are also more complex. Glycolipid BS, e.g. rhamnolipids (RL) and sophorolipids, are produced biologically as mixtures which vary in the size and saturation of the hydrophobic region as well as modifications in the hydrophilic headgroup, such as the number of sugar groups and different levels of acetylation, leading to variable surface-active properties. Their amphiphilicity allows RL to insert easily into membranes at sub-cmc concentrations to modulate membrane structure and extract lipopolysaccharides, leading to extensive biofilm remodeling in vivo, sometimes in collaboration with hydrophobic RL precursors. Thanks to their mosaicity, even anionic BS like RL only bind weakly to proteins and show much lower denaturing potency, even supporting membrane protein refolding. Nevertheless, they can promote protein degradation by proteases e.g. by neutralizing positive charges, which together with their biofilm-combating properties makes them very promising detergent surfactants. This article is part of a Special Issue entitled: Lipid order/lipid defects and lipid-control of protein activity edited by Dirk Schneider.

Efficient Reconstruction of Predictive Consensus Metabolic Network Models

Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.

Metabolic modelling reveals the specialization of secondary replicons for niche adaptation in Sinorhizobium meliloti

The genome of about 10% of bacterial species is divided among two or more large chromosome-sized replicons. The contribution of each replicon to the microbial life cycle (for example, environmental adaptations and/or niche switching) remains unclear. Here we report a genome-scale metabolic model of the legume symbiont Sinorhizobium meliloti that is integrated with carbon utilization data for 1,500 genes with 192 carbon substrates. Growth of S. meliloti is modelled in three ecological niches (bulk soil, rhizosphere and nodule) with a focus on the role of each of its three replicons. We observe clear metabolic differences during growth in the tested ecological niches and an overall reprogramming following niche switching. In silico examination of the inferred fitness of gene deletion mutants suggests that secondary replicons evolved to fulfil a specialized function, particularly host-associated niche adaptation. Thus, genes on secondary replicons might potentially be manipulated to promote or suppress host interactions for biotechnological purposes.

The genome of some bacteria consists of two or more chromosomes or replicons. Here, diCenzo et al. integrate genome-scale metabolic modelling and growth data from a collection of mutants of the plant symbiont Sinorhizobium meliloti to estimate the fitness contribution of each replicon in three environments.

Comparative genome-wide analysis reveals that Burkholderia contaminans MS14 possesses multiple antimicrobial biosynthesis genes but not major genetic loci required for pathogenesis

Burkholderia contaminans MS14 shows significant antimicrobial activities against plant and animal pathogenic fungi and bacteria. The antifungal agent occidiofungin produced by MS14 has great potential for development of biopesticides and pharmaceutical drugs. However, the use of Burkholderia species as biocontrol agent in agriculture is restricted due to the difficulties in distinguishing between plant growth-promoting bacteria and the pathogenic bacteria. The complete MS14 genome was sequenced and analyzed to find what beneficial and virulence-related genes it harbors. The phylogenetic relatedness of B. contaminans MS14 and other 17 Burkholderia species was also analyzed. To research MS14's potential virulence, the gene regions related to the antibiotic production, antibiotic resistance, and virulence were compared between MS14 and other Burkholderia genomes. The genome of B. contaminans MS14 was sequenced and annotated. The genomic analyses reveal the presence of multiple gene sets for antimicrobial biosynthesis, which contribute to its antimicrobial activities. BLAST results indicate that the MS14 genome harbors a large number of unique regions. MS14 is closely related to another plant growth-promoting Burkholderia strain B. lata 383 according to the average nucleotide identity data. Moreover, according to the phylogenetic analysis, plant growth-promoting species isolated from soils and mammalian pathogenic species are clustered together, respectively. MS14 has multiple antimicrobial activity-related genes identified from the genome, but it lacks key virulence-related gene loci found in the pathogenic strains. Additionally, plant growth-promoting Burkholderia species have one or more antimicrobial biosynthesis genes in their genomes as compared with nonplant growth-promoting soil-isolated Burkholderia species. On the other hand, pathogenic species harbor multiple virulence-associated gene loci that are not present in nonpathogenic Burkholderia species. The MS14 genome as well as Burkholderia species genome show considerable diversity. Multiple antimicrobial agent biosynthesis genes were identified in the genome of plant growth-promoting species of Burkholderia. In addition, by comparing to nonpathogenic Burkholderia species, pathogenic Burkholderia species have more characterized homologs of the gene loci known to contribute to pathogenicity and virulence to plant and animals.

Metabolic network-guided binning of metagenomic sequence fragments

MOTIVATION

Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health.

RESULTS

We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks.

AVAILABILITY AND IMPLEMENTATION

The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/

CONTACT

papin@virginia.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains

Background

Pseudomonas aeruginosa is an environmentally ubiquitous Gram-negative bacterium and important opportunistic human pathogen, causing severe chronic respiratory infections in patients with underlying conditions such as cystic fibrosis (CF) or bronchiectasis. In order to identify mechanisms responsible for adaptation during bronchiectasis infections, a bronchiectasis isolate, PAHM4, was phenotypically and genotypically characterized.

Results

This strain displays phenotypes that have been associated with chronic respiratory infections in CF including alginate over-production, rough lipopolysaccharide, quorum-sensing deficiency, loss of motility, decreased protease secretion, and hypermutation. Hypermutation is a key adaptation of this bacterium during the course of chronic respiratory infections and analysis indicates that PAHM4 encodes a mutated mutS gene responsible for a ~1,000-fold increase in mutation rate compared to wild-type laboratory strain P. aeruginosa PAO1. Antibiotic resistance profiles and sequence data indicate that this strain acquired numerous mutations associated with increased resistance levels to β-lactams, aminoglycosides, and fluoroquinolones when compared to PAO1. Sequencing of PAHM4 revealed a 6.38 Mbp genome, 5.9 % of which were unrecognized in previously reported P. aeruginosa genome sequences. Transcriptome analysis suggests a general down-regulation of virulence factors, while metabolism of amino acids and lipids is up-regulated when compared to PAO1 and metabolic modeling identified further potential differences between PAO1 and PAHM4.

Conclusions

This work provides insights into the potential differential adaptation of this bacterium to the lung of patients with bronchiectasis compared to other clinical settings such as cystic fibrosis, findings that should aid the development of disease-appropriate treatment strategies for P. aeruginosa infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2069-0) contains supplementary material, which is available to authorized users.

Potential of metabolomics to reveal Burkholderia cepacia complex pathogenesis and antibiotic resistance

The Burkholderia cepacia complex (Bcc) is a collection of closely related, genetically distinct, ecologically diverse species known to cause life-threatening infections in cystic fibrosis (CF) patients. By virtue of a flexible genomic structure and diverse metabolic activity, Bcc bacteria employ a wide array of virulence factors for pathogenesis in CF patients and have developed resistance to most of the commonly used antibiotics. However, the mechanism of pathogenesis and antibiotic resistance is still not fully understood. This mini review discusses the established and potential virulence determinants of Bcc and some of the contemporary strategies including transcriptomics and proteomics used to identify these traits. We also propose the application of metabolic profiling, a cost-effective modern-day approach to achieve new insights.

Metabolic network modeling of microbial communities

Genome-scale metabolic network reconstructions and constraint-based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome-scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the 'enzyme-soup' approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high-level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering.

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.