Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation

Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups

Bacillus subtilis is an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes of B. subtilis with 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divide B. subtilis strains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights into B. subtilis' metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats.

IMPORTANCE

As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. Bacillus subtilis strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 B. subtilis strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress

The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr resulted in decreased ATP levels and growth, despite increased rates of respiration or fermentation at appropriate oxygen tensions, suggesting that Δagr cells undergo a shift towards a hyperactive metabolic state in response to diminished metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived 'memory' of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Cybb-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.

Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress

The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr increased both respiration and fermentation but decreased ATP levels and growth, suggesting that Δagr cells assume a hyperactive metabolic state in response to reduced metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived "memory" of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Nox2-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.

Identification of key aroma compounds and core functional microorganisms associated with aroma formation for Monascus-fermented cheese

This study aimed to analyze the key aroma compounds and core functional microorganisms of Monascus-fermented cheese (MC). 36 key aroma compounds were identified according to gas chromatograph-mass spectrometer (GC-MS), aroma extract dilution analysis (AEDA), and odor activity values (OAV) analysis. And internal standard curves were used to clarify the changes in their concentration of them during cheese ripening. Furthermore, High-throughput sequencing was used to investigate the composition and dynamic changes of bacteria and fungi in MC, respectively. Lactococcus lactis was found to be the dominant bacterium while Monascus was confirmed to be the dominant fungus. In addition, Pearson correlation analysis showed that Lactococcus lactis, Staphylococcus, Trichococcus, and Monascus were strongly associated with the 36 key aroma compounds (r > 0.80, p < 0.05). Finally, a metabolic network containing biosynthetic pathways of the key aroma compounds was constructed. This study provides deeper insights into the unique aroma of MC and the contribution of cheese microbiota.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs

Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

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.

Stringent Response-Mediated Control of GTP Homeostasis Is Required for Long-Term Viability of Staphylococcus aureus

Staphylococcus aureus is an opportunistic bacterial pathogen that often results in difficult-to-treat infections. One mechanism used by S. aureus to enhance survival during infection is the stringent response. This is a stress survival pathway that utilizes the nucleotides (p)ppGpp to reallocate bacterial resources, shutting down growth until conditions improve. Small colony variants (SCVs) of S. aureus are frequently associated with chronic infections, and this phenotype has previously been linked to a hyperactive stringent response. Here, we examine the role of (p)ppGpp in the long-term survival of S. aureus under nutrient-restricted conditions. When starved, a (p)ppGpp-null S. aureus mutant strain ((p)ppGpp0) initially had decreased viability. However, after 3 days we observed the presence and dominance of a population of small colonies. Similar to SCVs, these small colony isolates (p0-SCIs) had reduced growth but remained hemolytic and sensitive to gentamicin, phenotypes that have been tied to SCVs previously. Genomic analysis of the p0-SCIs revealed mutations arising within gmk, encoding an enzyme in the GTP synthesis pathway. We show that a (p)ppGpp0 strain has elevated levels of GTP, and that the mutations in the p0-SCIs all lower Gmk enzyme activity and consequently cellular GTP levels. We further show that in the absence of (p)ppGpp, cell viability can be rescued using the GuaA inhibitor decoyinine, which artificially lowers the intracellular GTP concentration. Our study highlights the role of (p)ppGpp in GTP homeostasis and underscores the importance of nucleotide signaling for long-term survival of S. aureus in nutrient-limiting conditions, such as those encountered during infections. IMPORTANCE Staphylococcus aureus is a human pathogen that upon invasion of a host encounters stresses, such as nutritional restriction. The bacteria respond by switching on a signaling cascade controlled by the nucleotides (p)ppGpp. These nucleotides function to shut down bacterial growth until conditions improve. Therefore, (p)ppGpp are important for bacterial survival and have been implicated in promoting chronic infections. Here, we investigate the importance of (p)ppGpp for long-term survival of bacteria in nutrient-limiting conditions similar to those in a human host. We discovered that in the absence of (p)ppGpp, bacterial viability decreases due to dysregulation of GTP homeostasis. However, the (p)ppGpp-null bacteria were able to compensate by introducing mutations in the GTP synthesis pathway that led to a reduction in GTP build-up and a rescue of viability. This study therefore highlights the importance of (p)ppGpp for the regulation of GTP levels and for long-term survival of S. aureus in restricted environments.

Genome-Scale Metabolic Modeling Combined with Transcriptome Profiling Provides Mechanistic Understanding of Streptococcus thermophilus CH8 Metabolism

Streptococcus thermophilus is a lactic acid bacterium adapted toward growth in milk and is a vital component of starter cultures for milk fermentation. Here, we combine genome-scale metabolic modeling and transcriptome profiling to obtain novel metabolic insights into this bacterium. Notably, a refined genome-scale metabolic model (GEM) accurately representing S. thermophilus CH8 metabolism was developed. Modeling the utilization of casein as a nitrogen source revealed an imbalance in amino acid supply and demand, resulting in growth limitation due to the scarcity of specific amino acids, in particular sulfur amino acids. Growth experiments in milk corroborated this finding. A subtle interdependency of the redox balance and the secretion levels of the key metabolites lactate, formate, acetoin, and acetaldehyde was furthermore identified with the modeling approach, providing a mechanistic understanding of the factors governing the secretion product profile. As a potential effect of high expression of arginine biosynthesis genes, a moderate secretion of ornithine was observed experimentally, augmenting the proposed hypothesis of ornithine/putrescine exchange as part of the protocooperative interaction between S. thermophilus and Lactobacillus delbrueckii subsp. bulgaricus in yogurt. This study provides a foundation for future community modeling of food fermentations and rational development of starter strains with improved functionality.

IMPORTANCEStreptococcus thermophilus is one the main organisms involved in the fermentation of milk and, increasingly, also in the fermentation of plant-based foods. The construction of a functional high-quality genome-scale metabolic model, in conjunction with in-depth transcriptome profiling with a focus on metabolism, provides a valuable resource for the improved understanding of S. thermophilus physiology. An example is the model-based prediction of the most significant route of synthesis for the characteristic yogurt flavor compound acetaldehyde and identification of metabolic principles governing the synthesis of other flavor compounds. Moreover, the systematic assessment of amino acid supply and demand during growth in milk provides insights into the key challenges related to nitrogen metabolism that is imposed on S. thermophilus and any other organism associated with the milk niche.

A deep learning framework for identifying essential proteins based on multiple biological information

Background

Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein–protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information.

Results

We put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable.

Conclusions

Our proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.

Staphylococcus aureus Does Not Synthesize Arginine from Proline under Physiological Conditions

The Gram-positive pathogen Staphylococcus aureus is the only bacterium known to synthesize arginine from proline via the arginine-proline interconversion pathway despite having genes for the well-conserved glutamate pathway. Since the proline-arginine interconversion pathway is repressed by CcpA-mediated carbon catabolite repression (CCR), CCR has been attributed to the arginine auxotrophy of S. aureus. Using ribose as a secondary carbon source, here, we demonstrate that S. aureus arginine auxotrophy is not due to CCR but due to the inadequate concentration of proline degradation product. Proline is degraded by proline dehydrogenase (PutA) into pyrroline-5-carboxylate (P5C). Although the PutA expression was fully induced by ribose, the P5C concentration remained insufficient to support arginine synthesis because P5C was constantly consumed by the P5C reductase ProC. When the P5C concentration was artificially increased by either PutA overexpression or proC deletion, S. aureus could synthesize arginine from proline regardless of carbon source. In contrast, when the P5C concentration was reduced by overexpression of proC, it inhibited the growth of the ccpA deletion mutant without arginine. Intriguingly, the ectopic expression of the glutamate pathway enzymes converted S. aureus into arginine prototroph. In an animal experiment, the arginine-proline interconversion pathway was not required for the survival of S. aureus. Based on these results, we concluded that S. aureus does not synthesize arginine from proline under physiological conditions. We also propose that arginine auxotrophy of S. aureus is not due to the CcpA-mediated CCR but due to the inactivity of the conserved glutamate pathway. IMPORTANCE Staphylococcus aureus is a versatile Gram-positive human pathogen infecting various human organs. The bacterium's versatility is partly due to efficient metabolic regulation via the carbon catabolite repression system (CCR). S. aureus is known to interconvert proline and arginine, and CCR represses the synthesis of both amino acids. However, when CCR is released by a nonpreferred carbon source, S. aureus can synthesize proline but not arginine. In this study, we show that, in S. aureus, the intracellular concentration of pyrroline-5-carboxylate (P5C), the degradation product of proline and the substrate of proline synthesis, is too low to synthesize arginine from proline. These results call into question the notion that S. aureus synthesizes arginine from proline.

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.

Identifying the essential nutritional requirements of the probiotic bacteria Bifidobacterium animalis and Bifidobacterium longum through genome-scale modeling

Although bifidobacteria are widely used as probiotics, their metabolism and physiology remain to be explored in depth. In this work, strain-specific genome-scale metabolic models were developed for two industrially and clinically relevant bifidobacteria, Bifidobacterium animalis subsp. lactis BB-12® and B. longum subsp. longum BB-46, and subjected to iterative cycles of manual curation and experimental validation. A constraint-based modeling framework was used to probe the metabolic landscape of the strains and identify their essential nutritional requirements. Both strains showed an absolute requirement for pantethine as a precursor for coenzyme A biosynthesis. Menaquinone-4 was found to be essential only for BB-46 growth, whereas nicotinic acid was only required by BB-12®. The model-generated insights were used to formulate a chemically defined medium that supports the growth of both strains to the same extent as a complex culture medium. Carbohydrate utilization profiles predicted by the models were experimentally validated. Furthermore, model predictions were quantitatively validated in the newly formulated medium in lab-scale batch fermentations. The models and the formulated medium represent valuable tools to further explore the metabolism and physiology of the two species, investigate the mechanisms underlying their health-promoting effects and guide the optimization of their industrial production processes.

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.

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.

A systematic strategy using a reconstructed genome-scale metabolic network for pathogen Streptococcuspneumoniae D39 to find novel potential drug targets

Streptococcus pneumoniae is a Gram-positive bacterium that is one of the major causes of various infections such as pneumonia, meningitis, otitis media and endocarditis. Since antibiotic resistance of S. pneumoniae is pointed out as a challenge in the treatment of these infections, more studies are required to focus on disease prevention. In this research, a first manually curated genome-scale metabolic network of the pathogen S. pneumoniae D39 was reconstructed based on its genome annotation data, and biochemical knowledge from literature and databases. The model was validated by amino acid auxotrophies, gene essentiality analysis, and different carbohydrate sources. Then, a two-stage strategy was developed to find target genes for growth reduction of the pathogen and their importance in the various infection sites. In the first stage, growth-associated genes were identified by integration of transcriptomic data with the model and in the second stage, the importance of each gene in the metabolism for growth was evaluated using principal component analysis. The reports presented in the literature confirm the effect of some found genes on the growth of S. pneumoniae.

Characterizing Phage-Host Interactions in a Simplified Human Intestinal Barrier Model

An intestinal epithelium model able to produce mucus was developed to provide an environment suitable for testing the therapeutic activity of gut bacteriophages. We show that Enterococcus faecalis adheres more effectively in the presence of mucus, can invade the intestinal epithelia and is able to translocate after damaging tight junctions. Furthermore, Enterococcus phage vB_EfaM_A2 (a member of Herelleviridae that possesses virion associated immunoglobin domains) was found to translocate through the epithelium in the presence and absence of its host bacteria. Phage A2 protected eukaryotic cells by reducing mortality and maintaining the structure of the cell layer structure. We suggest the mammalian cell model utilized within this study as an adaptable in vitro model that can be employed to enable a better understanding of phage-bacteria interactions and the protective impact of phage therapy relating to the intestinal epithelium.

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.

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django.

Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms.

Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen

The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.

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.

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism

BACKGROUND

Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown.

RESULTS

To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum.

CONCLUSIONS

In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes

Essential metabolic reactions are shaping constituents of metabolic networks, enabling viable and distinct phenotypes across diverse life forms. Here we analyse and compare modelling predictions of essential metabolic functions with experimental data and thereby identify core metabolic pathways in prokaryotes. Simulations of 15 manually curated genome-scale metabolic models were integrated with 36 large-scale gene essentiality datasets encompassing a wide variety of species of bacteria and archaea. Conservation of metabolic genes was estimated by analysing 79 representative genomes from all the branches of the prokaryotic tree of life. We find that essentiality patterns reflect phylogenetic relations both for modelling and experimental data, which correlate highly at the pathway level. Genes that are essential for several species tend to be highly conserved as opposed to non-essential genes which may be conserved or not. The tRNA-charging module is highlighted as ancestral and with high centrality in the networks, followed closely by cofactor metabolism, pointing to an early information processing system supplied by organic cofactors. The results, which point to model improvements and also indicate faults in the experimental data, should be relevant to the study of centrality in metabolic networks and ancient metabolism but also to metabolic engineering with prokaryotes.

If we tried to list every known chemical reaction within an organism–human, plant or even bacteria–we would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular species–prokaryotes–to understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.

IN SILICO GENOME-SCALE RECONSTRUCTION AND ANALYSIS OF THE SHEWANELLA LOIHICA PV-4 METABOLIC NETWORK

Microbial Fuel Cells (MFCs) are devices that generate electricity directly from organic compounds with microbes (electricigens) serving as anodic catalysts. As a novel environment-friendly energy source, MFCs have extensive practical value. Since the biological features and metabolic mechanism of electricigens have a great effect on the electricity production of MFCs, it is a big deal to screen strains with high electricity productivity for improving the power output of MFC. Reconstructions and simulations of metabolic networks are of significant help in studying the metabolism of microorganisms so as to guide gene engineering and metabolic engineering to improve their power-generating efficiency. Herein, we reconstructed a genome-scale constraint-based metabolic network model of Shewanella loihica PV-4, an important electricigen, based on its genomic functional annotations, reaction databases and published metabolic network models of seven microorganisms. The resulting network model iGX790 consists of 902 reactions (including 71 exchange reactions), 798 metabolites and 790 genes, covering the main pathways such as carbon metabolism, energy metabolism, amino acid metabolism, nucleic acid metabolism and lipid metabolism. Using the model, we simulated the growth rate, the maximal synthetic rate of ATP, the flux variability analysis of metabolic network, gene deletion and so on to examine the metabolism of S. loihica PV-4.

Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes

Listeria monocytogenes is a microorganism of great concern for the food industry and the cause of human foodborne disease. Therefore, novel methods of control are needed, and systems biology is one such approach to identify them. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including L. monocytogenes. The objective of the work presented here was to generate GEMs for six different strains of L. monocytogenes, and to both qualitatively and quantitatively validate these GEMs with experimental data to examine the diversity of metabolic capabilities of numerous strains from the three different serovar groups most associated with foodborne outbreaks and human disease. Following qualitative validation, 57 of the 95 carbon sources tested experimentally were present in the GEMs, and; therefore, these were the compounds from which comparisons could be drawn. Of these 57 compounds, agreement between in silico predictions and in vitro results for carbon source utilization ranged from 80.7% to 91.2% between strains. Nutrient utilization agreement between in silico predictions and in vitro results were also conducted for numerous nitrogen, phosphorous, and sulfur sources. Additionally, quantitative validation showed that the L. monocytogenes GEMs were able to generate in silico predictions for growth rate and growth yield that were strongly and significantly (p < 0.0013 and p < 0.0015, respectively) correlated with experimental results. These findings are significant because they show that these GEMs for L. monocytogenes are comparable to published GEMs of other organisms for agreement between in silico predictions and in vitro results. Therefore, as with the other GEMs, namely those for Escherichia coli, Staphylococcus aureus, Vibrio vulnificus, and Salmonella spp., they can be used to determine new methods of growth control and disease treatment.

Genome sequence of "Leucobacter massiliensis" sp. nov. isolated from human pharynx after travel to the 2014 Hajj

"Leucobacter massiliensis" strain 122RC15T sp. nov. is a new species within the genus Leucobacter. The genome of this strain is described here. It was isolated from the pharynx of a 76-year-old Algerian female after travelling from the 2014 Hajj. "Leucobacter massiliensis" is a Gram-positive, aerobic bacillus. Here we describe the features including complete genome and annotation of this strain. The 3 136 406-bp long genome contains 2797 protein-coding genes and 49 RNA genes.

Computational Systems Biology of Metabolism in Infection

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

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

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.

Gene essentiality, conservation index and co-evolution of genes in cyanobacteria

Cyanobacteria, a group of photosynthetic prokaryotes, dominate the earth with ~ 1015 g wet biomass. Despite diversity in habitats and an ancient origin, cyanobacterial phylum has retained a significant core genome. Cyanobacteria are being explored for direct conversion of solar energy and carbon dioxide into biofuels. For this, efficient cyanobacterial strains will need to be designed via metabolic engineering. This will require identification of target knockouts to channelize the flow of carbon toward the product of interest while minimizing deletions of essential genes. We propose "Gene Conservation Index" (GCI) as a quick measure to predict gene essentiality in cyanobacteria. GCI is based on phylogenetic profile of a gene constructed with a reduced dataset of cyanobacterial genomes. GCI is the percentage of organism clusters in which the query gene is present in the reduced dataset. Of the 750 genes deemed to be essential in the experimental study on S. elongatus PCC 7942, we found 494 to be conserved across the phylum which largely comprise of the essential metabolic pathways. On the contrary, the conserved but non-essential genes broadly comprise of genes required under stress conditions. Exceptions to this rule include genes such as the glycogen synthesis and degradation enzymes, deoxyribose-phosphate aldolase (DERA), glucose-6-phosphate 1-dehydrogenase (zwf) and fructose-1,6-bisphosphatase class1, which are conserved but non-essential. While the essential genes are to be avoided during gene knockout studies as potentially lethal deletions, the non-essential but conserved set of genes could be interesting targets for metabolic engineering. Further, we identify clusters of co-evolving genes (CCG), which provide insights that may be useful in annotation. Principal component analysis (PCA) plots of the CCGs are demonstrated as data visualization tools that are complementary to the conventional heatmaps. Our dataset consists of phylogenetic profiles for 23,643 non-redundant cyanobacterial genes. We believe that the data and the analysis presented here will be a great resource to the scientific community interested in cyanobacteria.

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.

Detecting the Significant Flux Backbone of Escherichia coli metabolism

The heterogeneity of computationally predicted reaction fluxes in metabolic networks within a single flux state can be exploited to detect their significant flux backbone. Here, we disclose the backbone of Escherichia coli, and compare it with the backbones of other bacteria. We find that, in general, the core of the backbones is mainly composed of reactions in energy metabolism corresponding to ancient pathways. In E. coli, the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism. Moreover, the consideration of different media leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful to trace simultaneously both its evolution and adaptation fingerprints.

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.

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 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.

Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

Predicting network functions with nested patterns

Identifying suitable patterns in complex biological interaction networks helps understanding network functions and allows for predictions at the pattern level: by recognizing a known pattern, one can assign its previously established function. However, current approaches fail for previously unseen patterns, when patterns overlap and when they are embedded into a new network context. Here we show how to conceptually extend pattern-based approaches. We define metabolite patterns in metabolic networks that formalize co-occurrences of metabolites. Our probabilistic framework decodes the implicit information in the networks' metabolite patterns to predict metabolic functions. We demonstrate the predictive power by identifying 'indicator patterns', for instance, for enzyme classification, by predicting directions of novel reactions and of known reactions in new network contexts, and by ranking candidate network extensions for gap filling. Beyond their use in improving genome annotations and metabolic network models, we expect that the concepts transfer to other network types.

Staphylococcus aureus induces hypoxia and cellular damage in porcine dermal explants

We developed a porcine dermal explant model to determine the extent to which Staphylococcus aureus biofilm communities deplete oxygen, change pH, and produce damage in underlying tissue. Microelectrode measurements demonstrated that dissolved oxygen (DO) in biofilm-free dermal tissue was 4.45 ± 1.17 mg/liter, while DO levels for biofilm-infected tissue declined sharply from the surface, with no measurable oxygen detectable in the underlying dermal tissue. Magnetic resonance imaging demonstrated that biofilm-free dermal tissue had a significantly lower relative effective diffusion coefficient (0.26 ± 0.09 to 0.30 ± 0.12) than biofilm-infected dermal tissue (0.40 ± 0.12 to 0.48 ± 0.12; P < 0.0001). Thus, the difference in DO level was attributable to biofilm-induced oxygen demand rather than changes in oxygen diffusivity. Microelectrode measures showed that pH within biofilm-infected explants was more alkaline than in biofilm-free explants (8.0 ± 0.17 versus 7.5 ± 0.15, respectively; P < 0.002). Cellular and nuclear details were lost in the infected explants, consistent with cell death. Quantitative label-free shotgun proteomics demonstrated that both proapoptotic programmed cell death protein 5 and antiapoptotic macrophage migration inhibitory factor accumulated in the infected-explant spent medium, compared with uninfected-explant spent media (1,351-fold and 58-fold, respectively), consistent with the cooccurrence of apoptosis and necrosis in the explants. Biofilm-origin proteins reflected an extracellular matrix-adapted lifestyle of S. aureus. S. aureus biofilms deplete oxygen, increase pH, and induce cell death, all factors that contribute to impede wound healing.

Autocatalytic sets in E. coli metabolism

Background

A central unsolved problem in early evolution concerns self-organization towards higher complexity in chemical reaction networks. In theory, autocatalytic sets have useful properties to help model such transitions. Autocatalytic sets are chemical reaction systems in which molecules belonging to the set catalyze the synthesis of other members of the set. Given an external supply of starting molecules – the food set – and the conditions that (i) all reactions are catalyzed by at least one molecule, and (ii) each molecule can be constructed from the food set by a sequence of reactions, the system becomes a reflexively autocatalytic food-generated network (RAF set). Autocatalytic networks and RAFs have been studied extensively as mathematical models for understanding the properties and parameters that influence self-organizational tendencies. However, despite their appeal, the relevance of RAFs for real biochemical networks that exist in nature has, so far, remained virtually unexplored.

Results

Here we investigate the best-studied metabolic network, that of Escherichia coli, for the existence of RAFs. We find that the largest RAF encompasses almost the entire E. coli cytosolic reaction network. We systematically study its structure by considering the impact of removing catalysts or reactions. We show that, without biological knowledge, finding the minimum food set that maintains a given RAF is NP-complete. We apply a randomized algorithm to find (approximately) smallest subsets of the food set that suffice to sustain the original RAF.

Conclusions

The existence of RAF sets within a microbial metabolic network indicates that RAFs capture properties germane to biological organization at the level of single cells. Moreover, the interdependency between the different metabolic modules, especially concerning cofactor biosynthesis, points to the important role of spontaneous (non-enzymatic) reactions in the context of early evolution.

Graphical Abstract

Analysis of Genome-scale Expression Network in Four Major Bacterial Residents of Cystic Fibrosis Lung

In polymicrobial communities where several species co-exist in a certain niche and consequently the possibility of interactions among species is very high, gene expression data sources can give better insights in to underlying adaptation mechanisms assumed by bacteria. Furthermore, several possible synergistic or antagonistic interactions among species can be investigated through gene expression comparisons. Lung is one of the habitats harboring several distinct pathogens during severe pulmonary disorders such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Expression data analysis of these lung residents can help to gain a better understanding on how these species interact with each other within the host cells. The first part of this paper deals with introducing available data sources for the major bacteria responsible for causing lung diseases and their genomic relations. In the second part, the main focus is on the studies concerning gene expression analyses of these species.

PREDICTION OF ESSENTIAL PROTEINS BASED ON EDGE CLUSTERING COEFFICIENT AND GENE ONTOLOGY INFORMATION

Essential proteins are important for the survival and development of organisms. Lots of centrality algorithms based on network topology have been proposed to detect essential proteins and achieve good results. However, most of them only focus on the network topology, but ignore the false positive (FP) interactions in protein–protein interaction (PPI) network. In this paper, gene ontology (GO) information is proposed to measure the reliability of the edges in PPI network and we propose a novel algorithm for identifying essential proteins, named EGC algorithm. EGC algorithm integrates topology character of PPI network and GO information. To validate the performance of EGC algorithm, we use EGC and other nine methods (DC, BC, CC, SC, EC, LAC, NC, PEC and CoEWC) to identify the essential proteins in the two different yeast PPI networks: DIP and MIPS. The results show that EGC is better than the other nine methods, which means adding GO information can help in predicting essential proteins.

Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction

Background

The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs).

Results

Here we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn’s disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact.

Conclusions

The consortium of iBif452 and iFap484 was applied to predict F. prausnitzii’s demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.

Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models

Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.