In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network

Preserving the efficacy of antibiotics to tackle antibiotic resistance

Different international agencies recognize that antibiotic resistance is one of the most severe human health problems that humankind is facing. Traditionally, the introduction of new antibiotics solved this problem but various scientific and economic reasons have led to a shortage of novel antibiotics at the pipeline. This situation makes mandatory the implementation of approaches to preserve the efficacy of current antibiotics. The concept is not novel, but the only action taken for such preservation had been the 'prudent' use of antibiotics, trying to reduce the selection pressure by reducing the amount of antibiotics. However, even if antibiotics are used only when needed, this will be insufficient because resistance is the inescapable outcome of antibiotics' use. A deeper understanding of the alterations in the bacterial physiology upon acquisition of resistance and during infection will help to design improved strategies to treat bacterial infections. In this article, we discuss the interconnection between antibiotic resistance (and antibiotic activity) and bacterial metabolism, particularly in vivo, when bacteria are causing infection. We discuss as well how understanding evolutionary trade-offs, as collateral sensitivity, associated with the acquisition of resistance may help to define evolution-based therapeutic strategies to fight antibiotic resistance and to preserve currently used antibiotics.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Systems Biology: New Insight into Antibiotic Resistance

Over the past few decades, antimicrobial resistance (AMR) has emerged as an important threat to public health, resulting from the global propagation of multidrug-resistant strains of various bacterial species. Knowledge of the intrinsic factors leading to this resistance is necessary to overcome these new strains. This has contributed to the increased use of omics technologies and their extrapolation to the system level. Understanding the mechanisms involved in antimicrobial resistance acquired by microorganisms at the system level is essential to obtain answers and explore options to combat this resistance. Therefore, the use of robust whole-genome sequencing approaches and other omics techniques such as transcriptomics, proteomics, and metabolomics provide fundamental insights into the physiology of antimicrobial resistance. To improve the efficiency of data obtained through omics approaches, and thus gain a predictive understanding of bacterial responses to antibiotics, the integration of mathematical models with genome-scale metabolic models (GEMs) is essential. In this context, here we outline recent efforts that have demonstrated that the use of omics technology and systems biology, as quantitative and robust hypothesis-generating frameworks, can improve the understanding of antibiotic resistance, and it is hoped that this emerging field can provide support for these new efforts.

Microbiome systems biology advancements for natural well-being

Throughout the years all data from epidemiological, physiological and omics have suggested that the microbial communities play a considerable role in modulating human health. The population of microorganisms residing in the human intestine collectively known as microbiota presents a genetic repertoire that is higher in magnitude than the human genome. They play an essential role in host immunity and neuronal signaling. Rapid enhancement of sequence based screening and development of humanized gnotobiotic model has sparked a great deal of interest among scientists to probe the dynamic interactions of the commensal bacteria. This review focuses on systemic analysis of the gut microbiome to decipher the complexity of the host-microbe intercommunication and gives a special emphasis on the evolution of targeted precision medicine through microbiome engineering. In addition, we have also provided a comprehensive description of how interconnection between metabolism and biochemical reactions in a specific organism can be obtained from a metabolic network or a flux balance analysis and combining multiple datasets helps in the identification of a particular metabolite. The review highlights how genetic modification of the critical components and programming the resident microflora can be employed for targeted precision medicine. Inspite of the ongoing debate on the utility of gut microbiome we have explored on the probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also recapitulates integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet-microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of the disease. Inspite of the ongoing debate on the utility of the gut microbiome we have explored how probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also summarises integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet- microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from the microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of disease.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation.

Best Practices for Preparation of Staphylococcus aureus Metabolomics Samples

Developments in mass spectrometry have made it possible to identify individual biomolecules in complex samples. This has led to advances in the detection and quantification of both extracellular and intracellular metabolites, such as amino acids, organic acids, fatty acids, nucleotides, and CoA-esters from growth media and cellular extracts. However, the reproducibility of metabolite data can be problematic if the concentrations and/or stability of metabolites fluctuate during culture harvesting and processing. Herein we describe a standardized and efficient collection protocol and best practices for preservation and harvesting of Staphylococcus aureus cellular and supernatant samples to improve reproducibility, reliability, and consistency in mass-spectrometry-based metabolite data sets.

Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Genotypic and Phenotypic Characterization of Fecal Staphylococcus epidermidis Isolates Suggests Plasticity to Adapt to Different Human Body Sites

Staphylococcus epidermidis is a commensal species that has been increasingly identified as a nosocomial agent. Despite the interest, little is known about the ability of S. epidermidis isolates to adapt to different ecological niches through comparisons at genotype or phenotype levels. One niche where S. epidermidis has been reported is the human gut. Here, we present three S. epidermidis strains isolated from feces and show that they are not phylogenetically distinct from S. epidermidis isolated from other human body sites. Both gut and skin strains harbored multiple genes associated with biofilm formation and showed similar levels of biofilm formation on abiotic surfaces. High-throughput physiological tests using the BIOLOG technology showed no major metabolic differences between isolates from stool, skin, or cheese, while an isolate from bovine mastitis showed more phenotypic variation. Gut and skin isolates showed the ability to metabolize glycine-conjugated bile acids and to grow in the presence of bile, but the gut isolates exhibited faster anaerobic growth compared to isolates of skin origin.

An integrated computational and experimental study to investigate Staphylococcus aureus metabolism

Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understand its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 nonessential mutants from the Nebraska Transposon Mutant Library was performed, and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 863 metabolic genes, 1379 metabolites (including 1159 unique metabolites), and 1545 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data, and therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

Integration of in vivo experiment with a newly developed model of Staphylococcus aureus metabolism helps explore its metabolic versatility. A multidisciplinary team led by Rajib Saha at the University of Nebraska developed a new genome-scale metabolic model of the multi-drug resistant pathogen S. aureus by combining genome annotation data, reaction stoichiometry, and condition- and mutant-specific regulations from biochemical databases and previous strain-specific models. Extensive manual curation and incorporation of newly generated experimental data on growth and metabolite production improved the accuracy and predictability of the model and increased its ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

Carprofen-induced depletion of proton motive force reverses TetK-mediated doxycycline resistance in methicillin-resistant Staphylococcus pseudintermedius

We previously showed that doxycycline (DOX) and carprofen (CPF), a veterinary non-steroidal anti-inflammatory drug, have synergistic antimicrobial activity against methicillin-resistant Staphylococcus pseudintermedius (MRSP) carrying the tetracycline resistance determinant TetK. To elucidate the molecular mechanism of this synergy, we investigated the effects of the two drugs, individually and in combination, using a comprehensive approach including RNA sequencing, two-dimensional differential in-gel electrophoresis, macromolecule biosynthesis assays and fluorescence spectroscopy. Exposure of TetK-positive MRSP to CPF alone resulted in upregulation of pathways that generate ATP and NADH, and promote the proton gradient. We showed that CPF is a proton carrier that dissipates the electrochemical potential of the membrane. In the presence of both CPF and DOX, the energy compensation strategy was attenuated by downregulation of all the processes involved, such as citric acid cycle, oxidative phosphorylation and ATP-providing arginine deiminase pathway. Furthermore, protein biosynthesis inhibition increased from 20% under DOX exposure alone to 75% upon simultaneous exposure to CPF. We conclude that synergistic interaction of the drugs restores DOX susceptibility in MRSP by compromising proton-motive-force-dependent TetK-mediated efflux of the antibiotic. MRSP is unable to counterbalance CPF-mediated PMF depletion by cellular metabolic adaptations, resulting in intracellular accumulation of DOX and inhibition of protein biosynthesis.

Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications

Background

A genome-scale metabolic network model (GEM) is a mathematical representation of an organism’s metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains.

Objectives

In the present study, we have evaluated the predictive power of two GEMs, namely iBsu1103 (for Bacillus subtilis 168) and iMZ1055 (for Bacillus megaterium WSH002).

Materials and Methods

For comparing the predictive power of Bacillus subtilis and Bacillus megaterium GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used.

Results

By using the wealth of data in the literature, we evaluated the accuracy of in silico simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where B. subtilis and B. megaterium do not have similar phenotypes.

Conclusions

Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two Bacillus species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.

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.

Rationally designing antisense therapy to keep up with evolving bacterial resistance

Antisense molecules used as antibiotics offer the potential to keep up with acquired resistance, by redesigning the sequence of an antisense. Once bacteria acquire resistance by mutating the targeted sequence, new antisense can readily be designed by using sequence information of a target gene. However, antisense molecules require additional delivery vehicles to get into bacteria and be protected from degradation. Based on progress in the last few years it appears that, while redesigning or finding new delivery vehicle will be more difficult than redesigning the antisense cargo, it will perhaps be less difficult than finding new conventional small molecule antibiotics. In this study we propose a protocol that maximizes the combined advantages of engineered delivery vehicle and antisense cargo by decreasing the immediate growth advantage to the pathogen of mutating the entry mechanisms and increasing the advantage to the pathogen of antisense target mutations. Using this protocol, we show by computer simulation an appropriately designed antisense therapy can potentially be effective many times longer than conventional antibiotics before succumbing to resistance. While the simulations describe an in-vitro situation, based on comparison with other in-vitro studies on acquired resistance we believe the advantages of the combination antisense strategy have the potential to provide much more sustainability in vivo than conventional antibiotic therapy.

A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types

S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus’ metabolic response to its environment.

Environmental perturbations (e.g., antibiotic stress, nutrient starvation, oxidative stress) induce systems-level perturbations of bacterial cells that vary depending on the growth environment. The generation of omics data is aimed at capturing a complete view of the organism’s response under different conditions. Genome-scale models (GEMs) of metabolism represent a knowledge-based platform for the contextualization and integration of multi-omic measurements and can serve to offer valuable insights of system-level responses. This work provides the most up to date reconstruction effort integrating recent advances in the knowledge of S. aureus molecular biology with previous annotations resulting in the first quantitatively and qualitatively validated S. aureus GEM. GEM guided predictions obtained from model analysis provided insights into the effects of medium composition on metabolic flux distribution and gene essentiality. The model can also serve as a platform to guide network reconstructions for other Staphylococci as well as direct hypothesis generation following the integration of omics data sets, including transcriptomics, proteomics, metabolomics, and multi-strain genomic data.

Multiple pathways towards reduced membrane potential and concomitant reduction in aminoglycoside susceptibility in Staphylococcus aureus

Staphylococcus aureus is responsible for life-threatening and difficult-to-treat infections worldwide and antimicrobial resistance is an increasing concern. Whilst acquired resistance has been widely studied, little is known of the contributions from chromosomal determinants that upon inactivation may reduce the susceptibility of S. aureus to antibiotics. The aim of this study was to identify genetic determinants that upon inactivation reduce aminoglycoside susceptibility in S. aureus. The Nebraska Transposon Mutant Library of 1920 single-gene inactivations in S. aureus strain JE2 was screened for reduced susceptibility to gentamicin. Nine mutants were confirmed by Etest to display between 2- and 16-fold reduced susceptibility to this antibiotic. All of the identified genes were associated with the electron transport chain and energy metabolism. Four mutant strains (menD, hemB, aroC and SAUSA300_0355) conferred the largest decrease in gentamicin susceptibility and three exhibited a small colony variant phenotype, whereas the remaining mutants (qoxA, qoxB, qoxC, ndh and hemX) displayed colony morphology similar to the wild-type. All of the mutants, except hemX, displayed reduced membrane potential suggesting that reduced uptake of gentamicin is the predominant mechanism leading to reduced susceptibility. The results of this study demonstrate that S. aureus possesses multiple genes that upon inactivation by mutagenesis reduce the membrane potential and thereby reduce the lethal activity of gentamicin.

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.

Effect of Rocket (Eruca sativa) Extract on MRSA Growth and Proteome: Metabolic Adjustments in Plant-Based Media

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) in food has provoked a great concern about the presence of MRSA in associated foodstuff. Although MRSA is often detected in various retailed meat products, it seems that food handlers are more strongly associated with this type of food contamination. Thus, it can be easily postulated that any food could be contaminated with this pathogen in an industrial environment or in household and cause food poisoning. To this direction, the effect of rocket (Eruca sativa) extract on MRSA growth and proteome was examined in the present study. This goal was achieved with the comparative study of the MRSA strain COL proteome, cultivated in rocket extract versus the standard Luria-Bertani growth medium. The obtained results showed that MRSA was able to grow in rocket extract. In addition, proteome analysis using 2-DE method showed that MRSA strain COL is taking advantage of the sugar-, lipid-, and vitamin-rich substrate in the liquid rocket extract, although its growth was delayed in rocket extract compared to Luria–Bertani medium. This work could initiate further research about bacterial metabolism in plant-based media and defense mechanisms against plant-derived antibacterials.

Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

BACKGROUND

Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship.

RESULTS

We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium's metabolism, such as changes in the bacterium's growth in response to different environmental conditions.

CONCLUSIONS

After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.

A systems biology approach to investigate the antimicrobial activity of oleuropein

Oleuropein and its hydrolysis products are olive phenolic compounds that have antimicrobial effects on a variety of pathogens, with the potential to be utilized in food and pharmaceutical products. While the existing research is mainly focused on individual genes or enzymes that are regulated by oleuropein for antimicrobial activities, little work has been done to integrate intracellular genes, enzymes and metabolic reactions for a systematic investigation of antimicrobial mechanism of oleuropein. In this study, the first genome-scale modeling method was developed to predict the system-level changes of intracellular metabolism triggered by oleuropein in Staphylococcus aureus, a common food-borne pathogen. To simulate the antimicrobial effect, an existing S. aureus genome-scale metabolic model was extended by adding the missing nitric oxide reactions, and exchange rates of potassium, phosphate and glutamate were adjusted in the model as suggested by previous research to mimic the stress imposed by oleuropein on S. aureus. The developed modeling approach was able to match S. aureus growth rates with experimental data for five oleuropein concentrations. The reactions with large flux change were identified and the enzymes of fifteen of these reactions were validated by existing research for their important roles in oleuropein metabolism. When compared with experimental data, the up/down gene regulations of 80% of these enzymes were correctly predicted by our modeling approach. This study indicates that the genome-scale modeling approach provides a promising avenue for revealing the intracellular metabolism of oleuropein antimicrobial properties.

Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species

Background

Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia.

Results

We developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies.

Conclusions

Our biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0334-8) contains supplementary material, which is available to authorized users.

Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

Metabolic modeling of clostridia: current developments and applications

Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.

Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica

Chlorophyll is one of the most important pigments present in green plants and rice is one of the major food crops consumed worldwide. We curated the existing genome scale metabolic model (GSM) of rice leaf by incorporating new compartment, reactions and transporters. We used this modified GSM to elucidate how the chlorophyll is synthesized in a leaf through a series of bio-chemical reactions spanned over different organelles using inorganic macronutrients and light energy. We predicted the essential reactions and the associated genes of chlorophyll synthesis and validated against the existing experimental evidences. Further, ammonia is known to be the preferred source of nitrogen in rice paddy fields. The ammonia entering into the plant is assimilated in the root and leaf. The focus of the present work is centered on rice leaf metabolism. We studied the relative importance of ammonia transporters through the chloroplast and the cytosol and their interlink with other intracellular transporters. Ammonia assimilation in the leaves takes place by the enzyme glutamine synthetase (GS) which is present in the cytosol (GS1) and chloroplast (GS2). Our results provided possible explanation why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air.

Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences?

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

Understanding the interactions between bacteria in the human gut through metabolic modeling

The human gut microbiome plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of disease. Genome-scale metabolic models (GEMs) can provide an increased understanding of the mechanisms behind the effects of diet, the genotype-phenotype relationship and microbial robustness. Here we reconstructed GEMs for three key species, (Bacteroidesthetaiotamicron, Eubacteriumrectale and Methanobrevibactersmithii) as relevant representatives of three main phyla in the human gut (Bacteroidetes, Firmicutes and Euryarchaeota). We simulated the interactions between these three bacteria in different combinations of gut ecosystems and compared the predictions with the experimental results obtained from colonization of germ free mice. Furthermore, we used our GEMs for analyzing the contribution of each species to the overall metabolism of the gut microbiota based on transcriptome data and demonstrated that these models can be used as a scaffold for understanding bacterial interactions in the gut.

Systems medicine: a new approach to clinical practice

Most respiratory diseases are considered complex diseases as their susceptibility and outcomes are determined by the interaction between host-dependent factors (genetic factors, comorbidities, etc.) and environmental factors (exposure to microorganisms or allergens, treatments received, etc.) The reductionist approach in the study of diseases has been of fundamental importance for the understanding of the different components of a system. Systems biology or systems medicine is a complementary approach aimed at analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels. Systems medicine is currently used for the interpretation and understanding of the pathogenesis and pathophysiology of different diseases, biomarker discovery, design of innovative therapeutic targets, and the drawing up of computational models for different biological processes. In this review we discuss the most relevant concepts of the theory underlying systems medicine, as well as its applications in the various biological processes in humans.

Staphylococcus aureus metabolic response to changing environmental conditions - a metabolomics perspective

Microorganisms preserve their metabolic function against a wide range of external perturbations including biotic or abiotic factors by utilizing cellular adaptations to maintain cell homeostasis. Functional genomics aims to detect such adaptive alterations on the level of transcriptome, proteome and metabolome to understand system wide changes and to identify interactions between the different levels of biochemical organization. Microbial metabolomics measures metabolites, the direct biochemical response to the environment, and is pivotal to the understanding of the variability and dynamics of bacterial cell metabolism. Metabolomics can measure many different types of compounds including primary metabolites, secondary metabolites, second messengers, quorum sensing compounds and others, which all contribute to the complex bacterial response to an environmental change. Recent data confirmed that many metabolic processes in pathogenic bacteria are linked to virulence and invasive capabilities. Deciphering bacterial metabolism in response to specific environmental conditions and in specific genetic backgrounds will help map the complex network between the metabolome and the other "-omes". Here, we will review a selection of case studies for the pathogenic Gram-positive bacterium Staphylococcus aureus and summarize the current state of metabolomics literature covering staphylococci metabolism under different physiological states.

A systems-level approach for investigating Pseudomonas aeruginosa biofilm formation

Prevention of the initiation of biofilm formation is the most important step for combating biofilm-associated pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once biofilms are formed. Genes essential to bacterial growth in the planktonic state are potential targets to treat biofilm-associated pathogens. However, the biofilm formation capability of strains with mutations in these essential genes must be evaluated, since the pathogen might form a biofilm before it is eliminated. In order to address this issue, this work proposes a systems-level approach to quantifying the biofilm formation capability of mutants to determine target genes that are essential for bacterial metabolism in the planktonic state but do not induce biofilm formation in their mutants. The changes of fluxes through the reactions associated with the genes positively related to biofilm formation are used as soft sensors in the flux balance analysis to quantify the trend of biofilm formation upon the mutation of an essential gene. The essential genes whose mutants are predicted not to induce biofilm formation are regarded as gene targets. The proposed approach was applied to identify target genes to treat Pseudomonas aeruginosa infections. It is interesting to find that most essential gene mutants exhibit high potential to induce the biofilm formation while most non-essential gene mutants do not. Critically, we identified four essential genes, lysC, cysH, adk, and galU, that constitute gene targets to treat P. aeruginosa. They have been suggested by existing experimental data as potential drug targets for their crucial role in the survival or virulence of P. aeruginosa. It is also interesting to find that P. aeruginosa tends to survive the essential-gene mutation treatment by mainly enhancing fluxes through 8 metabolic reactions that regulate acetate metabolism, arginine metabolism, and glutamate metabolism.

Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.

Changes in the Staphylococcus aureus transcriptome during early adaptation to the lung

Staphylococcus aureus is a common inhabitant of the human nasopharynx. It is also a cause of life-threatening illness, producing a potent array of virulence factors that enable survival in normally sterile sites. The transformation of S. aureus from commensal to pathogen is poorly understood. We analyzed S. aureus gene expression during adaptation to the lung using a mouse model of S. aureus pneumonia. Bacteria were isolated by bronchoalveolar lavage after residence in vivo for up to 6 hours. S. aureus in vivo RNA transcription was compared by microarray to that of shake flask grown stationary phase and early exponential phase cells. Compared to in vitro conditions, the in vivo transcriptome was dramatically altered within 30 minutes. Expression of central metabolic pathways changed significantly in response to the lung environment. Gluconeogenesis (fbs, pckA) was down regulated, as was TCA cycle and fermentation pathway gene expression. Genes associated with amino acid synthesis, RNA translation and nitrate respiration were upregulated, indicative of a highly active metabolic state during the first 6 hours in the lung. Virulence factors regulated by agr were down regulated in vivo and in early exponential phase compared to stationary phase cells. Over time in vivo, expression of ahpCF, involved in H₂O₂ scavenging, and uspA, which encodes a universal stress regulator, increased. Transcription of leukotoxic α and β-type phenol-soluble modulins psmα1-4 and psmβ1-2 increased 13 and 8-fold respectively; hld mRNA, encoding δ-hemolysin, was increased 9-fold. These were the only toxins to be significantly upregulated in vivo. These data provide the first complete survey of the S. aureus transcriptome response to the mammalian airway. The results present intriguing contrasts with previous work in other in vitro and in vivo models and provide novel insights into the adaptive and temporal response of S. aureus early in the pathogenesis of pneumonia.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BACKGROUND

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.

RESULTS

A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.

CONCLUSIONS

FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

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.

Using the reconstructed genome-scale human metabolic network to study physiology and pathology

Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology and pharmacology. There are currently more than 20 publications that utilize Recon 1, including studies of cancer, diabetes, host-pathogen interactions, heritable metabolic disorders and off-target drug binding effects. In this mini-review, we focus on the reconstruction of the global human metabolic network and four classes of its application. We show that computational simulations for numerous pathologies have yielded clinically relevant results, many corroborated by existing or newly generated experimental data.

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.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

Metabolic regulation of antibiotic resistance

It is generally assumed that antibiotics and resistance determinants are the task forces of a biological warfare in which each resistance determinant counteracts the activity of a specific antibiotic. According to this view, antibiotic resistance might be considered as a specific response to an injury, not necessarily linked to bacterial metabolism, except for the burden that the acquisition of resistance might impose on the bacteria (fitness costs). Nevertheless, it is known that changes in bacterial metabolism, such as those associated with dormancy or biofilm formation, modulate bacterial susceptibility to antibiotics (phenotypic resistance), indicating that there exists a linkage between bacterial metabolism and antibiotic resistance. The analyses of the intrinsic resistomes of bacterial pathogens also demonstrate that the building up of intrinsic resistance requires the concerted action of many elements, several of which play a relevant role in the bacterial metabolism. In this article, we will review the current knowledge on the linkage between bacterial metabolism and antibiotic resistance and will discuss the role of global metabolic regulators such as Crc in bacterial susceptibility to antibiotics. Given that growing into the human host requires a metabolic adaptation, we will discuss whether this adaptation might trigger resistance even in the absence of selective pressure by antibiotics.

Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

BACKGROUND

Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets.

RESULTS

We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets.

CONCLUSIONS

As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.

Staphylococcus aureus physiological growth limitations: insights from flux calculations built on proteomics and external metabolite data

Comparing proteomics and metabolomics allows insights into Staphylococcus aureus physiological growth. We update genome and proteome information and deliver strain-specific metabolic models for three S. aureus strains (COL, N315, and Newman). We find a number of differences in metabolism and enzymes. Growth experiments (glucose or combined with oxygen limitation) were conducted to measure external metabolites. Fluxes of the central metabolism were calculated from these data with low error. In exponential phase, glycolysis is active and amino acids are used for growth. In later phases, dehydroquinate synthetase is suppressed and acetate metabolism starts. There are strain-specific differences for these phases. A time series of 2-D gel protein expression data on COL strain delivered a second data set (glucose limitation) on which fluxes were calculated. The comparison with the metabolite-predicted fluxes shows, in general, good correlation. Outliers point to different regulated enzymes for S. aureus COL under these limitations. In exponential growth, there is lower activity for some enzymes in upper glycolysis and pentose phosphate pathway and stronger activity for some in lower glycolysis. In transition phase, aspartate kinase is expressed to meet amino acid requirements and in later phases there is high expression of glyceraldehyde-3-phosphate dehydrogenase and lysine synthetase. Central metabolite fluxes and protein expression of their enzymes correlate in S. aureus.

A metabolomic view of Staphylococcus aureus and its ser/thr kinase and phosphatase deletion mutants: involvement in cell wall biosynthesis

Little is known about intracellular metabolite pools in pathogens such as Staphylococcus aureus. We have studied a particular metabolome by means of the presented LC-MS method. By investigating the central carbon metabolism which includes most of the energy transfer molecules like nucleotides, sugar mono- and biphosphates, and cofactors, a conclusion about phenotypes and stress answers in microorganisms is possible. Quantitative metabolite levels of S. aureus grown in complex lysogeny broth and in minimal medium were compared in the wild-type S. aureus strain 8325 and the isogenic eukaryotic-like protein serine/threonine kinase (DeltapknB) and phosphatase (Deltastp) deletion mutants. Detection of several remarkable differences, e.g., in nucleotide metabolism and especially cell wall precursor metabolites, indicates a previously unreported importance of serine/threonine kinase/phosphatase on peptidoglycan and wall teichoic acid biosynthesis. These findings may lead to new insights into the regulation of staphylococcal cell wall metabolism.