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

Investigation of the Mechanism and Chemistry Underlying Staphylococcus aureus' Ability to Inhibit Pseudomonas aeruginosa Growth In Vitro

Pseudomonas aeruginosa inhibits or eradicates Staphylococcus aureus in most in vitro settings. Nonetheless, P. aeruginosa and S. aureus are commonly isolated from chronically infected, nonhealing wounds and lungs of people with cystic fibrosis (CF). Therefore, we hypothesized that S. aureus could protect itself from P. aeruginosa through glucose-derived metabolites, such as small organic acids, preventing it from being eradicated. This in vitro study demonstrated that S. aureus populations, in the presence of glucose, secrete one or more substances that efficiently eradicate P. aeruginosa in a concentration-dependent manner. These substances had a molecular mass lower than three kDa, were hydrophilic, heat- and proteinase-resistant, and demonstrated a pH-dependent effect. Nuclear magnetic resonance analysis identified acetoin, acetic acid, and oligopeptides or cyclic peptides in glucose-grown S. aureus supernatants. All the tested wild-type and clinical S. aureus strain inhibited P. aeruginosa growth. Thus, we proposed a model in which a cocktail of these compounds, produced by established S. aureus populations in glucose presence, facilitated these two species' coexistence in chronic infections. IMPORTANCE Chronic infections affect a growing part of the population and are associated with high societal and personal costs. Multiple bacterial species are often present in these infections, and multispecies infections are considered more severe than single-species infections. Staphylococcus aureus and Pseudomonas aeruginosa often coexist in chronic infections. However, the interactions between these two species and their coexistence in chronic infections are not fully understood. By exploring in vitro interactions, we found a novel S. aureus-mediated inhibition of P. aeruginosa, and we suggested a model of the coexistence of the two species in chronic infections. With this study, we enhanced our understanding of the pathogenesis of chronic multispecies infections, which is crucial to paving the way for developing improved treatment strategies.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

Topological Analysis of the Carbon-Concentrating CETCH Cycle and a Photorespiratory Bypass Reveals Boosted CO2-Sequestration by Plants

Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some in planta-tested synthetic carbon-concentrating cycles (CCCs) hold promise to increase plant biomass while diminishing atmospheric carbon dioxide burden. Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin-Benson-Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide-harvesting potential in plants with an AP3 bypass and CETCH-AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.

Staphylococcusaureus Transcriptome Data and Metabolic Modelling Investigate the Interplay of Ser/Thr Kinase PknB, Its Phosphatase Stp, the glmR/yvcK Regulon and the cdaA Operon for Metabolic Adaptation

Serine/threonine kinase PknB and its corresponding phosphatase Stp are important regulators of many cell functions in the pathogen S. aureus. Genome-scale gene expression data of S. aureus strain NewHG (sigB⁺) elucidated their effect on physiological functions. Moreover, metabolic modelling from these data inferred metabolic adaptations. We compared wild-type to deletion strains lacking pknB, stp or both. Ser/Thr phosphorylation of target proteins by PknB switched amino acid catabolism off and gluconeogenesis on to provide the cell with sufficient components. We revealed a significant impact of PknB and Stp on peptidoglycan, nucleotide and aromatic amino acid synthesis, as well as catabolism involving aspartate transaminase. Moreover, pyrimidine synthesis was dramatically impaired by stp deletion but only slightly by functional loss of PknB. In double knockouts, higher activity concerned genes involved in peptidoglycan, purine and aromatic amino acid synthesis from glucose but lower activity of pyrimidine synthesis from glucose compared to the wild type. A second transcriptome dataset from S. aureus NCTC 8325 (sigB⁻) validated the predictions. For this metabolic adaptation, PknB was found to interact with CdaA and the yvcK/glmR regulon. The involved GlmR structure and the GlmS riboswitch were modelled. Furthermore, PknB phosphorylation lowered the expression of many virulence factors, and the study shed light on S. aureus infection 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.

Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.

Effects of glucose and oxygen on arginine metabolism by coagulase-negative staphylococci

Coagulase-negative staphylococci (CNS) are not only part of the desirable microbiota of fermented meat products but also commonly inhabit skin and flesh wounds. Their proliferation depends on the versatility to use energy sources and the adaptation to fluctuating environmental parameters. In this study, the conversion of the amino acid arginine by two strains with arginine deiminase (ADI) activity (Staphylococcus carnosus 833 and S. pasteuri αs3-13) and a strain with nitric oxide synthase (NOS) activity (S. haemolyticus G110) was modelled as a function of glucose and oxygen availability. Both factors moderately inhibited the ADI-based conversion kinetics, never leading to full repression. However, for NOS-driven conversion of arginine by S. haemolyticus G110, oxygen was an absolute requirement. When changing from microaerobic conditions to aerobiosis, a switch from homolactic fermentation to a combined formation of lactic acid, acetic acid, and acetoin was found in all cases, after which lactic acid and acetic acid were used as substrates. The kinetic model proposed provided a suitable description of the data of glucose and arginine co-metabolism as a function of oxygen levels and may serve as a tool to further analyse the behaviour of staphylococci in different ecosystems or when applying specific food processing conditions.

Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

Background

Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain α522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.

Results

Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.

Conclusions

Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3616-7) contains supplementary material, which is available to authorized users.

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

A Staphylococcus aureus Proteome Overview: Shared and Specific Proteins and Protein Complexes from Representative Strains of All Three Clades

Staphylococcus aureus is an important model organism and pathogen. This S. aureus proteome overview details shared and specific proteins and selected virulence-relevant protein complexes from representative strains of all three major clades. To determine the strain distribution and major clades we used a refined strain comparison combining ribosomal RNA, MLST markers, and looking at highly-conserved regions shared between strains. This analysis shows three sub-clades (A–C) for S. aureus. As calculations are complex and strain annotation is quite time consuming we compare here key representatives of each clade with each other: model strains COL, USA300, Newman, and HG001 (clade A), model strain N315 and Mu50 (clade B) and ED133 and MRSA252 (clade C). We look at these individual proteomes and compare them to a background of 64 S. aureus strains. There are overall 13,284 S. aureus proteins not part of the core proteome which are involved in different strain-specific or more general complexes requiring detailed annotation and new experimental data to be accurately delineated. By comparison of the eight representative strains, we identify strain-specific proteins (e.g., 18 in COL, 105 in N315 and 44 in Newman) that characterize each strain and analyze pathogenicity islands if they contain such strain-specific proteins. We identify strain-specific protein repertoires involved in virulence, in cell wall metabolism, and phosphorylation. Finally we compare and analyze protein complexes conserved and well-characterized among S. aureus (a total of 103 complexes), as well as predict and analyze several individual protein complexes, including structure modeling in the three clades.

Annotation of the Staphylococcus aureus Metabolome Using Liquid Chromatography Coupled to High-Resolution Mass Spectrometry and Application to the Study of Methicillin Resistance

Staphylococcus aureus can cause a variety of severe disease patterns and can readily acquire antibiotic resistance; however, the mechanisms by which this commensal becomes a pathogen or develops antibiotic resistance are still poorly understood. Here we asked whether metabolomics can be used to distinguish bacterial strains with different antibiotic susceptibilities. Thus, an efficient and robust method was first thoroughly implemented to measure the intracellular metabolites of S. aureus in an unbiased and reproducible manner. We also placed special emphasis on metabolome coverage and annotation and used both hydrophilic interaction liquid chromatography and pentafluorophenyl-propyl columns coupled to high-resolution mass spectrometry in conjunction with our spectral database developed in-house to identify with high confidence as many meaningful S. aureus metabolites as possible. Overall, we were able to characterize up to 210 metabolites in S. aureus, which represents a substantial ∼50% improvement over previously published data. We then preliminarily compared the metabolic profiles of 10 clinically relevant methicillin-resistant and susceptible strains harvested at different time points during the exponential growth phase (without any antibiotic exposure). Interestingly, the resulting data revealed a distinct behavior of "slow-growing" resistant strains, which show modified levels of several precursors of peptidoglycan and capsular polysaccharide biosynthesis.

Modelling antibiotic and cytotoxic isoquinoline effects in Staphylococcus aureus, Staphylococcus epidermidis and mammalian cells

Isoquinolines (IQs) are natural substances with an antibiotic potential we aim to optimize. Specifically, IQ-238 is a synthetic analog of the novel-type N,C-coupled naphthylisoquinoline (NIQ) alkaloid ancisheynine. Recently, we developed and tested other IQs such as IQ-143. By utilizing genome-wide gene expression data, metabolic network modelling and Voronoi tessalation based data analysis - as well as cytotoxicity measurements, chemical properties calculations and principal component analysis of the NIQs - we show that IQ-238 has strong antibiotic potential for staphylococci and low cytotoxicity against murine or human cells. Compared to IQ-143, systemic effects are less pronounced. Most enzyme activity changes due to IQ-238 are located in the carbohydrate metabolism. Validation includes metabolite measurements on biological replicates. IQ-238 delineates key properties and a chemical space for a good therapeutic window. The combination of analysis methods allows suggestions for further lead development and yields an in-depth look at staphylococcal adaptation and network changes after antibiosis. Results are compared to eukaryotic host cells.

Elementary mode analysis reveals that Clostridium acetobutylicum modulates its metabolic strategy under external stress

Clostridium acetobutylicumis a strict anaerobe which exhibits two distinct steps in its metabolic network.

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.

Effect of oxygen on glucose metabolism: utilization of lactate in Staphylococcus aureus as revealed by in vivo NMR studies

The ability to successfully adapt to changing host conditions is crucial for full virulence of bacterial pathogens. Staphylococcus aureus has to cope with fluctuating oxygen concentrations during the course of infection. Hence, we studied the effect of oxygen on glucose metabolism in non-growing S. aureus COL-S cells by in vivo¹³C-NMR. Glucose catabolism was probed at different oxygen concentrations in suspensions of cells grown aerobically (direct effects on metabolism) or anaerobically (transcriptional adjustment to oxygen deprivation). In aerobically-grown cells, the rate of glucose consumption diminished progressively with decreasing oxygen concentrations. Additionally, oxygen deprivation resulted in biphasic glucose consumption, with the second phase presenting a higher rate. The fructose-1,6-bisphosphate pool peaked while glucose was still abundant, but the transient maximum varied with the oxygen concentration. As oxygen became limiting mannitol/mannitol-1-phosphate were detected as products of glucose catabolism. Under anoxic conditions, accumulation of mannitol-1-phosphate ceased with the switch to higher glucose consumption rates, which implies the activation of a more efficient means by which NAD⁺ can be regenerated. The distribution of end-products deriving from glucose catabolism was dramatically affected by oxygen: acetate increased and lactate decreased with the oxygen concentration; ethanol was formed only anaerobically. Moreover, oxygen promoted the energetically favourable conversion of lactate into acetate, which was particularly noticeable under fully oxygenated conditions. Interestingly, under aerobiosis growing S. aureus cells also converted lactate to acetate, used simultaneously glucose and lactate as substrates for growth, and grew considerably well on lactate-medium. We propose that the efficient lactate catabolism may endow S. aureus with a metabolic advantage in its ecological niche.

Analyzing Thiol-Dependent Redox Networks in the Presence of Methylene Blue and Other Antimalarial Agents with RT-PCR-Supported in silico Modeling

Background

In the face of growing resistance in malaria parasites to drugs, pharmacological combination therapies are important. There is accumulating evidence that methylene blue (MB) is an effective drug against malaria. Here we explore the biological effects of both MB alone and in combination therapy using modeling and experimental data.

Results

We built a model of the central metabolic pathways in P. falciparum. Metabolic flux modes and their changes under MB were calculated by integrating experimental data (RT-PCR data on mRNAs for redox enzymes) as constraints and results from the YANA software package for metabolic pathway calculations. Several different lines of MB attack on Plasmodium redox defense were identified by analysis of the network effects. Next, chloroquine resistance based on pfmdr/and pfcrt transporters, as well as pyrimethamine/sulfadoxine resistance (by mutations in DHF/DHPS), were modeled in silico. Further modeling shows that MB has a favorable synergism on antimalarial network effects with these commonly used antimalarial drugs.

Conclusions

Theoretical and experimental results support that methylene blue should, because of its resistance-breaking potential, be further tested as a key component in drug combination therapy efforts in holoendemic areas.

Metabolic adaptation and protein complexes in prokaryotes

Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations.

Software applications toward quantitative metabolic flux analysis and modeling

Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches.

Salmonella enterica: a surprisingly well-adapted intracellular lifestyle

The infectious intracellular lifestyle of Salmonella enterica relies on the adaptation to nutritional conditions within the Salmonella-containing vacuole (SCV) in host cells. We summarize latest results on metabolic requirements for Salmonella during infection. This includes intracellular phenotypes of mutant strains based on metabolic modeling and experimental tests, isotopolog profiling using ¹³C-compounds in intracellular Salmonella, and complementation of metabolic defects for attenuated mutant strains towards a comprehensive understanding of the metabolic requirements of the intracellular lifestyle of Salmonella. Helpful for this are also genomic comparisons. We outline further recent studies and which analyses of intracellular phenotypes and improved metabolic simulations were done and comment on technical required steps as well as progress involved in the iterative refinement of metabolic flux models, analyses of mutant phenotypes, and isotopolog analyses. Salmonella lifestyle is well-adapted to the SCV and its specific metabolic requirements. Salmonella metabolism adapts rapidly to SCV conditions, the metabolic generalist Salmonella is quite successful in host infection.