Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes

Fungal organic acid uptake of mineral-derived K is dependent on distance from carbon hotspot

IMPORTANCE

Fungal species are foundational members of soil ecosystems with vital contributions that support interspecies resource translocation. The minute details of these biogeochemical processes are poorly investigated. Here, we addressed this knowledge gap by probing fungal growth in a novel mineral-doped soil micromodel platform using spatially-resolved imaging methodologies. We found that fungi uptake K from K-rich minerals using organic acids exuded in a distance-dependent manner from a carbon-rich hotspot. While identification of specific mechanisms within soil remains challenging, our findings demonstrate the significance of reduced complexity platforms such as the mineral-doped micromodel in probing biogeochemical processes. These findings provide visualization into hyphal uptake and transport of mineral-derived nutrients in a resource-limited environment.

Elucidating regulatory processes of intense physical activity by multi-omics analysis

BACKGROUND

Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.

METHODS

To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of three biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- versus pre-exercise were compared by Student's t-test followed by pathway analysis and comparison between the different omics modalities.

RESULTS

Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.

CONCLUSION

This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.

How Bacterial Pathogens Coordinate Appetite with Virulence

Cells adjust growth and metabolism to nutrient availability. Having access to a variety of carbon sources during infection of their animal hosts, facultative intracellular pathogens must efficiently prioritize carbon utilization. Here, we discuss how carbon source controls bacterial virulence, with an emphasis on Salmonella enterica serovar Typhimurium, which causes gastroenteritis in immunocompetent humans and a typhoid-like disease in mice, and propose that virulence factors can regulate carbon source prioritization by modifying cellular physiology. On the one hand, bacterial regulators of carbon metabolism control virulence programs, indicating that pathogenic traits appear in response to carbon source availability. On the other hand, signals controlling virulence regulators may impact carbon source utilization, suggesting that stimuli that bacterial pathogens experience within the host can directly impinge on carbon source prioritization. In addition, pathogen-triggered intestinal inflammation can disrupt the gut microbiota and thus the availability of carbon sources. By coordinating virulence factors with carbon utilization determinants, pathogens adopt metabolic pathways that may not be the most energy efficient because such pathways promote resistance to antimicrobial agents and also because host-imposed deprivation of specific nutrients may hinder the operation of certain pathways. We propose that metabolic prioritization by bacteria underlies the pathogenic outcome of an infection.

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.

Combined transcriptomic and metabolomic analysis of Salmonella in the presence or absence of PhoP-PhoQ system under low Mg2+ conditions

INTRODUCTION

Previous reports revealed the role played by Salmonella PhoP-PhoQ system in virulence activation, antimicrobial tolerance and intracellular survival, the impact of PhoP-PhoQ on cell metabolism has been less extensively described.

OBJECTIVES

The aim of this study is to address whether and how the PhoP-PhoQ system affects the cell metabolism of Salmonella.

METHODS

We constructed a Salmonella phoP deletion mutant strain TT-81 (PhoP-OFF), a Salmonella PhoP constitutively expressed strain TT-82 (PhoP-ON) and a wild-type Salmonella PhoP strain TT-80 (PhoP-N), using P22-mediated generalized transduction or λ Red-mediated targeted mutagenesis. We then measured the in vitro growth kinetics of all test strains and determined their metabolomic and transcriptomic profiles using gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) and RNA-seq technique, respectively.

RESULTS

Low-Mg²⁺ conditions impaired the growth of the phoP deletion mutant strain TT-81 (PhoP-OFF) dramatically. 42 metabolites in the wild-type PhoP strain TT-80 (PhoP-N) and 28 metabolites in the PhoP constitutively expressed strain TT-82 (PhoP-ON) changed by the absence of phoP. In contrast, the level of 19 compounds in TT-80 (PhoP-N) changed comparing to the PhoP constitutively expressed strain TT-82 (PhoP-N). The mRNA level of 95 genes in TT-80 (PhoP-N) changed when phoP was disrupted, wherein 78 genes downregulated and 17 genes upregulated. 106 genes were determined to be differentially expressed between TT-81 (PhoP-OFF) and TT-82 (PhoP-ON). While only 16 genes were found to differentially expressed between TT-82 (PhoP-ON) and TT-80 (PhoP-N).

CONCLUSION

Our findings confirmed the impact of PhoP-PhoQ system on lipopolysaccharide (LPS) modification, energy metabolism, and the biosynthesis or transport of amino acids. Most importantly, we demonstrated that the turnover of a given metabolite could respond differentially to the level of phoP. Taken together, the present study provided new insights into the adaptation of Salmonella to the host environment and helped to characterize the impact of the PhoP-PhoQ system on the cell metabolism.

Bulk and Spatially Resolved Extracellular Metabolome of Free-Living Nitrogen Fixation

Soil nitrogen (N) transformations constrain terrestrial net primary productivity and are driven by the activity of soil microorganisms. Free-living N fixation (FLNF) is an important soil N transformation and key N input to terrestrial systems, but the forms of N contributed to soil by FLNF are poorly understood. To address this knowledge gap, a focus on microorganisms and microbial scale processes is needed that links N-fixing bacteria and their contributed N sources to FLNF process rates. However, studying the activity of soil microorganisms in situ poses inherent challenges, including differences in sampling scale between microorganism and process rates, which can be addressed with culture-based studies and an emphasis on microbial-scale measurements. Culture conditions can differ significantly from soil conditions, so it also important that such studies include multiple culture conditions like liquid and solid media as proxies for soil environments like soil pore water and soil aggregate surfaces. Here we characterized extracellular N-containing metabolites produced by two common, diazotrophic soil bacteria in liquid and solid media, with or without N, across two sampling scales (bulk via GC-MS and spatially resolved via MALDI mass spec imaging). We found extracellular production of inorganic and organic N during FLNF, indicating terrestrial N contributions from FLNF occur in multiple forms not only as ammonium as previously thought. Extracellular metabolite profiles differed between liquid and solid media supporting previous work indicating environmental structure influences microbial function. Metabolite profiles also differed between sampling scales underscoring the need to quantify microbial scale conditions to accurately interpret microbial function.

IMPORTANCE Free-living nitrogen-fixing bacteria contribute significantly to terrestrial nitrogen availability; however, the forms of nitrogen contributed by this process are poorly understood. This is in part because of inherent challenges to studying soil microorganisms in situ, such as vast differences in scale between microorganism and ecosystem and complexities of the soil system (e.g., opacity, chemical complexity). Thus, upscaling important ecosystem processes driven by soil microorganisms, like free-living nitrogen fixation, requires microbial-scale measurements in controlled systems. Our work generated bulk and spatially resolved measurements of nitrogen released during free-living nitrogen fixation under two contrasting growth conditions analogous to soil pores and aggregates. This work allowed us to determine that diverse forms of nitrogen are likely contributed to terrestrial systems by free-living nitrogen bacteria. We also demonstrated that microbial habitat (e.g., liquid versus solid media) alters microbial activity and that measurement of microbial activity is altered by sampling scale (e.g., bulk versus spatially resolved) highlighting the critical importance of quantifying microbial-scale processes to upscaling of ecosystem function.

Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology

Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.

A Histoplasma capsulatum Lipid Metabolic Map Identifies Antifungal Targets

Lipids play a fundamental role in fungal cell biology, being essential cell membrane components and major targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here, we built a comprehensive map of the Histoplasma capsulatum lipid metabolic pathway by incorporating proteomic and lipidomic analyses. We performed genetic complementation and overexpression of H. capsulatum genes in Saccharomyces cerevisiae to validate reactions identified in the map and to determine enzymes responsible for catalyzing orphan reactions. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We found that the sphingolipid biosynthesis inhibitor myriocin, the fatty acid desaturase inhibitor thiocarlide, and the fatty acid analog 10-thiastearic acid inhibit H. capsulatum growth in nanomolar to low-micromolar concentrations. These compounds also reduced the intracellular infection in an alveolar macrophage cell line. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development.

Fate, cytotoxicity and cellular metabolomic impact of ingested nanoscale carbon dots using simulated digestion and a triculture small intestinal epithelial model

Carbon dots (CDs) are a promising material currently being explored in many industrial applications in the biomedical and agri-food areas; however, studies supporting the environmental health risk assessment of CDs are needed. This study focuses on various CD forms including iron (FeCD) and copper (CuCD) doped CDs synthesized using hydrothermal method, their fate in gastrointestinal tract, and their cytotoxicity and potential changes to cellular metabolome in a triculture small intestinal epithelial model. Physicochemical characterization revealed that 75% of Fe in FeCD and 95% of Cu in CuCD were dissolved during digestion. No significant toxic effects were observed for pristine CDs and FeCDs. However, CuCD induced significant dose-dependent toxic effects including decreases in TEER and cell viability, increases in cytotoxicity and ROS production, and alterations in important metabolites, including D-glucose, L-cysteine, uridine, citric acid and multiple fatty acids. These results support the current understanding that pristine CDs are relatively non-toxic and the cytotoxicity is dependent on the doping molecules.

Cryptococcus neoformans-Infected Macrophages Release Proinflammatory Extracellular Vesicles: Insight into Their Components by Multi-omics

Cryptococcus neoformans causes cryptococcal meningitis, which is frequent in patients with HIV/AIDS, especially in less-developed countries. The incidence of cryptococcal meningitis is close to 1 million each year globally.

Cryptococcus neoformans causes deadly mycosis in immunocompromised individuals. Macrophages are key cells fighting against microbes. Extracellular vesicles (EVs) are cell-to-cell communication mediators. The roles of EVs from infected host cells in the interaction with Cryptococcus remain uninvestigated. Here, EVs from viable C. neoformans-infected macrophages reduced fungal burdens but led to shorter survival of infected mice. In vitro, EVs induced naive macrophages to an inflammatory phenotype. Transcriptome analysis showed that EVs from viable C. neoformans-infected macrophages activated immune-related pathways, including p53 in naive human and murine macrophages. Conserved analysis demonstrated that basic cell biological processes, including cell cycle and division, were activated by infection-derived EVs from both murine and human infected macrophages. Combined proteomics, lipidomics, and metabolomics of EVs from infected macrophages showed regulation of pathways such as extracellular matrix (ECM) receptors and phosphatidylcholine. This form of intermacrophage communication could serve to prepare cells at more distant sites of infection to resist C. neoformans infection.

Cryptococcus neoformans Secretes Small Molecules That Inhibit IL-1β Inflammasome-Dependent Secretion

Cryptococcus neoformans is an encapsulated yeast that causes disease mainly in immunosuppressed hosts. It is considered a facultative intracellular pathogen because of its capacity to survive and replicate inside phagocytes, especially macrophages. This ability is heavily dependent on various virulence factors, particularly the glucuronoxylomannan (GXM) component of the polysaccharide capsule. Inflammasome activation in phagocytes is usually protective against fungal infections, including cryptococcosis. Nevertheless, recognition of C. neoformans by inflammasome receptors requires specific changes in morphology or the opsonization of the yeast, impairing proper inflammasome function. In this context, we analyzed the impact of molecules secreted by C. neoformans B3501 strain and its acapsular mutant Δcap67 in inflammasome activation in an in vitro model. Our results showed that conditioned media derived from B3501 was capable of inhibiting inflammasome-dependent events (i.e., IL-1β secretion and LDH release via pyroptosis) more strongly than conditioned media from Δcap67, regardless of GXM presence. We also demonstrated that macrophages treated with conditioned media were less responsive against infection with the virulent strain H99, exhibiting lower rates of phagocytosis, increased fungal burdens, and enhanced vomocytosis. Moreover, we showed that the aromatic metabolite DL-Indole-3-lactic acid (ILA) and DL-p-Hydroxyphenyllactic acid (HPLA) were present in B3501's conditioned media and that ILA alone or with HPLA is involved in the regulation of inflammasome activation by C. neoformans. These results were confirmed by in vivo experiments, where exposure to conditioned media led to higher fungal burdens in Acanthamoeba castellanii culture as well as in higher fungal loads in the lungs of infected mice. Overall, the results presented show that conditioned media from a wild-type strain can inhibit a vital recognition pathway and subsequent fungicidal functions of macrophages, contributing to fungal survival in vitro and in vivo and suggesting that secretion of aromatic metabolites, such as ILA, during cryptococcal infections fundamentally impacts pathogenesis.

Remodeling of the Histoplasma Capsulatum Membrane Induced by Monoclonal Antibodies

Antibodies play a central role in host immunity by directly inactivating or recognizing an invading pathogen to enhance different immune responses to combat the invader. However, the cellular responses of pathogens to the presence of antibodies are not well-characterized. Here, we used different mass spectrometry techniques to study the cellular responses of the pathogenic fungus Histoplasma capsulatum to monoclonal antibodies (mAb) against HSP60, the surface protein involved in infection. A proteomic analysis of H. capsulatum yeast cells revealed that mAb binding regulates a variety of metabolic and signaling pathways, including fatty acid metabolism, sterol metabolism, MAPK signaling and ubiquitin-mediated proteolysis. The regulation of the fatty acid metabolism was accompanied by increases in the level of polyunsaturated fatty acids, which further augmented the degree of unsaturated lipids in H. capsulatum's membranes and energy storage lipids, such as triacylglycerols, phosphatidylcholines, phosphatidylethanolamines and phosphatidylinositols. MAb treatment also regulated sterol metabolism by increasing the levels of cholesterol and ergosterol in the cells. We also showed that global changes in the lipid profiles resulted in an increased susceptibility of H. capsulatum to the ergosterol-targeting drug amphotericin B. Overall, our data showed that mAb induction of global changes in the composition of H. capsulatum membranes can potentially impact antifungal treatment during histoplasmosis.

The stringent response regulator (p) ppGpp mediates virulence gene expression and survival in Erwinia amylovora

Background

The nucleotide second messengers, i.e., guanosine tetraphosphate and pentaphosphate [collectively referred to as (p) ppGpp], trigger the stringent response under nutrient starvation conditions and play an essential role in virulence in the fire blight pathogen Erwinia amylovora. Here, we present transcriptomic analyses to uncover the overall effect of (p) ppGpp-mediated stringent response in E. amylovora in the hrp-inducing minimal medium (HMM).

Results

In this study, we investigated the transcriptomic changes of the (p) ppGpp⁰ mutant under the type III secretion system (T3SS)-inducing condition using RNA-seq. A total of 1314 differentially expressed genes (DEGs) was uncovered, representing more than one third (36.8%) of all genes in the E. amylovora genome. Compared to the wild-type, the (p) ppGpp⁰ mutant showed down-regulation of genes involved in peptide ATP-binding cassette (ABC) transporters and virulence-related processes, including type III secretion system (T3SS), biofilm, and motility. Interestingly, in contrast to previous reports, the (p) ppGpp⁰ mutant showed up-regulation of amino acid biosynthesis genes, suggesting that it might be due to that these amino acid biosynthesis genes are indirectly regulated by (p) ppGpp in E. amylovora or represent specific culturing condition used. Furthermore, the (p) ppGpp⁰ mutant exhibited up-regulation of genes involved in translation, SOS response, DNA replication, chromosome segregation, as well as biosynthesis of nucleotide, fatty acid and lipid.

Conclusion

These findings suggested that in HMM environment, E. amylovora might use (p) ppGpp as a signal to activate virulence gene expression, and simultaneously mediate the balance between virulence and survival by negatively regulating DNA replication, translation, cell division, as well as biosynthesis of nucleotide, amino acid, fatty acid, and lipid. Therefore, (p) ppGpp could be a promising target for developing novel control measures to fight against this devastating disease of apples and pears.

NMR-Metabolomics Shows That BolA Is an Important Modulator of Salmonella Typhimurium Metabolic Processes under Virulence Conditions

BolA is a ubiquitous global transcription factor. Despite its clear role in the induction of important stress-resistant physiological changes and its recent implication in the virulence of Salmonella, further research is required to shed light on the pathways modulated by BolA. In this study, we resorted to untargeted ¹H-NMR metabolomics to understand the impact of BolA on the metabolic profile of Salmonella Typhimurium, under virulence conditions. Three strains of S. Typhimurium SL1344 were studied: An SL1344 strain transformed with an empty plasmid (control), a bolA knockout mutant (ΔbolA), and a strain overexpressing bolA (bolA⁺). These strains were grown in a minimal virulence-inducing medium and cells were collected at the end of the exponential and stationary phases. The extracts were analyzed by NMR, and multivariate and univariate statistical analysis were performed to identify significant alterations. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of ¹H-NMR data allowed the discrimination between the metabolic profiles of these strains, revealing increased levels of acetate, valine, alanine, NAD⁺, succinate, coenzyme A, glutathione, and putrescine in bolA⁺. These results indicate that BolA regulates pathways related to stress resistance and virulence, being an important modulator of the metabolic processes needed for S. Typhimurium infection.

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model of Danio rerio

Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.

Multi-omics Signature of Candida auris, an Emerging and Multidrug-Resistant Pathogen

Candida auris is a recently described pathogenic fungus that is causing invasive outbreaks on all continents. The fungus is of high concern given the numbers of multidrug-resistant strains that have been isolated in distinct sites across the globe. The fact that its diagnosis is still problematic suggests that the spreading of the pathogen remains underestimated. Notably, the molecular mechanisms of virulence and antifungal resistance employed by this new species are largely unknown. In the present work, we compared two clinical isolates of C. auris with distinct drug susceptibility profiles and a Candida albicans reference strain using a multi-omics approach. Our results show that, despite the distinct drug resistance profile, both C. auris isolates appear to be very similar, albeit with a few notable differences. However, compared to C. albicans both C. auris isolates have major differences regarding their carbon utilization and downstream lipid and protein content, suggesting a multifactorial mechanism of drug resistance. The molecular profile displayed by C. auris helps to explain the antifungal resistance and virulence phenotypes of this new emerging pathogen.IMPORTANCE Candida auris was first described in Japan in 2009 and has now been the cause of significant outbreaks across the globe. The high number of isolates that are resistant to one or more antifungals, as well as the high mortality rates from patients with bloodstream infections, has attracted the attention of the medical mycology, infectious disease, and public health communities to this pathogenic fungus. In the current work, we performed a broad multi-omics approach on two clinical isolates isolated in New York, the most affected area in the United States and found that the omic profile of C. auris differs significantly from C. albicans In addition to our insights into C. auris carbon utilization and lipid and protein content, we believe that the availability of these data will enhance our ability to combat this rapidly emerging pathogenic yeast.

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

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

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges, since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for the simultaneous extraction of metabolites, proteins, and lipids (MPLEx) from the same soil sample was created by adapting a solvent-based approach. This MPLEx protocol has proven to be both simple and robust for many sample types, even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.

Blocking hexose entry into glycolysis activates alternative metabolic conversion of these sugars and upregulates pentose metabolism in Aspergillus nidulans

Background

Plant biomass is the most abundant carbon source for many fungal species. In the biobased industry fungi, are used to produce lignocellulolytic enzymes to degrade agricultural waste biomass. Here we evaluated if it would be possible to create an Aspergillus nidulans strain that releases, but does not metabolize hexoses from plant biomass. For this purpose, metabolic mutants were generated that were impaired in glycolysis, by using hexokinase (hxkA) and glucokinase (glkA) negative strains. To prevent repression of enzyme production due to the hexose accumulation, strains were generated that combined these mutations with a deletion in creA, the repressor involved in regulating preferential use of different carbon catabolic pathways.

Results

Phenotypic analysis revealed reduced growth for the hxkA1 glkA4 mutant on wheat bran. However, hexoses did not accumulate during growth of the mutants on wheat bran, suggesting that glucose metabolism is re-routed towards alternative carbon catabolic pathways. The creAΔ4 mutation in combination with preventing initial phosphorylation in glycolysis resulted in better growth than the hxkA/glkA mutant and an increased expression of pentose catabolic and pentose phosphate pathway genes. This indicates that the reduced ability to use hexoses as carbon sources created a shift towards the pentose fraction of wheat bran as a major carbon source to support growth.

Conclusion

Blocking the direct entry of hexoses to glycolysis activates alternative metabolic conversion of these sugars in A. nidulans during growth on plant biomass, but also upregulates conversion of other sugars, such as pentoses.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4609-x) contains supplementary material, which is available to authorized users.

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.

Properties and mechanism of d-glucosaminate-6-phosphate ammonia-lyase: An aminotransferase family enzyme with d-amino acid specificity

Salmonella enterica serovar Typhimurium utilizes a wide range of growth substrates, some of which are relatively novel. One of these unusual substrates is d-glucosaminate, which is metabolized by the enzymes encoded in the dga operon. d-Glucosaminate is transported and converted to d-glucosaminate-6-phosphate (G6P) by a phosphotransferase system, composed of DgaABCD. The protein product of dgaE, d-glucosaminate-6-phosphate ammonia lyase (DGL), converts G6P to 2-keto-3-deoxygluconate-6-phosphate, which undergoes a retroaldol reaction catalyzed by the DgaF protein to give d-glyceraldehyde-3-phosphate and pyruvate. We have now developed an improved synthesis of G6P which gives a higher yield. The DGL reaction is of mechanistic interest because it is one of only a few enzymes in the pyridoxal-5'-phosphate (PLP) dependent aminotransferase superfamily known to catalyze reaction of a d-amino acid substrate. The pH dependence of DGL shows an optimum at 7.5-8.5, suggesting a requirement for a catalytic base. α-Glycerophosphate and inorganic phosphate are weak competitive inhibitors, with K_i values near 30mM, and d-serine is neither a substrate nor an inhibitor. We have found in rapid-scanning stopped-flow experiments that DGL reacts rapidly with its substrate to form a quinonoid intermediate with λ_max=480nm, within the dead time (ca. 2msec), which then rapidly decays (k=279s^-1) to an intermediate with absorption between 330 and 350nm, probably an aminoacrylate complex. We suggest a mechanism for DGL and propose that the unusual stereochemistry of the DGL reaction requires a catalytic base poised on the opposite face of the PLP-substrate complex from the other members of the aminotransferase superfamily.

A Genome-Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium

Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h^-1 respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome

Although the gut microbiome plays important roles in host physiology, health and disease¹, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system² to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level.

KEY WORDS

Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

Bacterial peptide transporters: Messengers of nutrition to virulence

Bacteria possess numerous peptide transporters for importing peptides as nutrients. However, these peptide transporters are now consistently reported to play a role in the virulence of various bacterial pathogens. Their ability to transport peptides has implications in antibacterial therapy as well. Therefore, it would be instrumental to have complete knowledge about the role of peptide transporters in mediating this cross connection between metabolism and pathogenesis. Studies on various peptide transporters in bacterial pathogens have improved our understanding of this field. In this review, we have given an overview of the functioning of bacterial peptide transporters and their contribution in virulence of major bacterial pathogens.

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.

Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical).

IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.

Author Video: An author video summary of this article is available.

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Multi-omics analysis reveals regulators of the response to nitrogen limitation in Yarrowia lipolytica

Background

Yarrowia lipolytica is an oleaginous ascomycete yeast that stores lipids in response to limitation of nitrogen. While the enzymatic pathways responsible for neutral lipid accumulation in Y. lipolytica are well characterized, regulation of these pathways has received little attention. We therefore sought to characterize the response to nitrogen limitation at system-wide levels, including the proteome, phosphoproteome and metabolome, to better understand how this organism regulates and controls lipid metabolism and to identify targets that may be manipulated to improve lipid yield.

Results

We found that ribosome structural genes are down-regulated under nitrogen limitation, during which nitrogen containing compounds (alanine, putrescine, spermidine and urea) are depleted and sugar alcohols and TCA cycle intermediates accumulate (citrate, fumarate and malate). We identified 1219 novel phosphorylation sites in Y. lipolytica, 133 of which change in their abundance during nitrogen limitation. Regulatory proteins, including kinases and DNA binding proteins, are particularly enriched for phosphorylation. Within lipid synthesis pathways, we found that ATP-citrate lyase, acetyl-CoA carboxylase and lecithin cholesterol acyl transferase are phosphorylated during nitrogen limitation while many of the proteins involved in β-oxidation are down-regulated, suggesting that storage lipid accumulation may be regulated by phosphorylation of key enzymes. Further, we identified short DNA elements that associate specific transcription factor families with up- and down-regulated genes.

Conclusions

Integration of metabolome, proteome and phosphoproteome data identifies lipid accumulation in response to nitrogen limitation as a two-fold result of increased production of acetyl-CoA from excess citrate and decreased capacity for β-oxidation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2471-2) contains supplementary material, which is available to authorized users.

Peptide utilizing carbon starvation gene yjiY is required for flagella mediated infection caused by Salmonella

Peptide metabolism forms an important part of the metabolic network of Salmonella and to acquire these peptides the pathogen possesses a number of peptide transporters. While various peptide transporters known in Salmonella are well studied, very little is known about the carbon starvation (cst) genes, cstA and yjiY, which are also predicted to be involved in peptide metabolism. We investigated the role of these genes in the metabolism and pathogenesis of Salmonella and demonstrated for the first time that cst genes actually participate in transport of specific peptides in Salmonella. Further, we established that the carbon starvation gene yjiY affects the expression of flagella leading to poor adhesion of the bacterium to host cells. In contrast with the previously reported role of the gene cstA in virulence of Salmonella in C. elegans, we showed that yjiY is required for successful colonization of Salmonella in the mouse gut. Thus, cst genes not only contribute to the metabolism of Salmonella but also influence its virulence.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Silymarin Suppresses Cellular Inflammation By Inducing Reparative Stress Signaling

Silymarin, a characterized extract of the seeds of milk thistle (Silybum marianum), suppresses cellular inflammation. To define how this occurs, transcriptional profiling, metabolomics, and signaling studies were performed in human liver and T cell lines. Cellular stress and metabolic pathways were modulated within 4 h of silymarin treatment: activation of Activating Transcription Factor 4 (ATF-4) and adenosine monophosphate protein kinase (AMPK) and inhibition of mammalian target of rapamycin (mTOR) signaling, the latter being associated with induction of DNA-damage-inducible transcript 4 (DDIT4). Metabolomics analyses revealed silymarin suppression of glycolytic, tricarboxylic acid (TCA) cycle, and amino acid metabolism. Anti-inflammatory effects arose with prolonged (i.e., 24 h) silymarin exposure, with suppression of multiple pro-inflammatory mRNAs and signaling pathways including nuclear factor kappa B (NF-κB) and forkhead box O (FOXO). Studies with murine knock out cells revealed that silymarin inhibition of both mTOR and NF-κB was partially AMPK dependent, whereas silymarin inhibition of mTOR required DDIT4. Other natural products induced similar stress responses, which correlated with their ability to suppress inflammation. Thus, natural products activate stress and repair responses that culminate in an anti-inflammatory cellular phenotype. Natural products like silymarin may be useful as tools to define how metabolic, stress, and repair pathways regulate cellular inflammation.

Comprehensive Metabolomic, Lipidomic and Microscopic Profiling of Yarrowia lipolytica during Lipid Accumulation Identifies Targets for Increased Lipogenesis

Yarrowia lipolytica is an oleaginous ascomycete yeast that accumulates large amounts of lipids and has potential as a biofuel producing organism. Despite a growing scientific literature focused on lipid production by Y. lipolytica, there remain significant knowledge gaps regarding the key biological processes involved. We applied a combination of metabolomic and lipidomic profiling approaches as well as microscopic techniques to identify and characterize the key pathways involved in de novo lipid accumulation from glucose in batch cultured, wild-type Y. lipolytica. We found that lipids accumulated rapidly and peaked at 48 hours during the five day experiment, concurrent with a shift in amino acid metabolism. We also report that exhaustion of extracellular sugars coincided with thickening of the cell wall, suggesting that genes involved in cell wall biogenesis may be a useful target for improving the efficiency of lipid producing yeast strains.

Diel metabolomics analysis of a hot spring chlorophototrophic microbial mat leads to new hypotheses of community member metabolisms

Dynamic environmental factors such as light, nutrients, salt, and temperature continuously affect chlorophototrophic microbial mats, requiring adaptive and acclimative responses to stabilize composition and function. Quantitative metabolomics analysis can provide insights into metabolite dynamics for understanding community response to such changing environmental conditions. In this study, we quantified volatile organic acids, polar metabolites (amino acids, glycolytic and citric acid cycle intermediates, nucleobases, nucleosides, and sugars), wax esters, and polyhydroxyalkanoates, resulting in the identification of 104 metabolites and related molecules in thermal chlorophototrophic microbial mat cores collected over a diel cycle in Mushroom Spring, Yellowstone National Park. A limited number of predominant taxa inhabit this community and their functional potentials have been previously identified through metagenomic and metatranscriptomic analyses and in situ metabolisms, and metabolic interactions among these taxa have been hypothesized. Our metabolomics results confirmed the diel cycling of photorespiration (e.g., glycolate) and fermentation (e.g., acetate, propionate, and lactate) products, the carbon storage polymers polyhydroxyalkanoates, and dissolved gasses (e.g., H2 and CO2) in the waters overlying the mat, which were hypothesized to occur in major mat chlorophototrophic community members. In addition, we have formulated the following new hypotheses: (1) the morning hours are a time of biosynthesis of amino acids, DNA, and RNA; (2) photo-inhibited cells may also produce lactate via fermentation as an alternate metabolism; (3) glycolate and lactate are exchanged among Synechococcus and Roseiflexus spp.; and (4) fluctuations in many metabolite pools (e.g., wax esters) at different times of day result from species found at different depths within the mat responding to temporal differences in their niches.

BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology

Background

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.

Results

We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.

Conclusions

BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0486-3) contains supplementary material, which is available to authorized users.

Salmonella-how a metabolic generalist adopts an intracellular lifestyle during infection

The human-pathogenic bacterium Salmonella enterica adjusts and adapts to different environments while attempting colonization. In the course of infection nutrient availabilities change drastically. New techniques, "-omics" data and subsequent integration by systems biology improve our understanding of these changes. We review changes in metabolism focusing on amino acid and carbohydrate metabolism. Furthermore, the adaptation process is associated with the activation of genes of the Salmonella pathogenicity islands (SPIs). Anti-infective strategies have to take these insights into account and include metabolic and other strategies. Salmonella infections will remain a challenge for infection biology.

Metabolomics data normalization with EigenMS

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.

A Statistical Analysis of the Effects of Urease Pre-treatment on the Measurement of the Urinary Metabolome by Gas Chromatography-Mass Spectrometry

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography-mass spectrometry (GC-MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC-MS. In total, 235 GC-MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples 1) had the same or lower coefficients of variance among reproducibly detected metabolites, 2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and 3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pretreatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pretreatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.

Phototrophic biofilm assembly in microbial-mat-derived unicyanobacterial consortia: model systems for the study of autotroph-heterotroph interactions

Microbial autotroph-heterotroph interactions influence biogeochemical cycles on a global scale, but the diversity and complexity of natural systems and their intractability to in situ manipulation make it challenging to elucidate the principles governing these interactions. The study of assembling phototrophic biofilm communities provides a robust means to identify such interactions and evaluate their contributions to the recruitment and maintenance of phylogenetic and functional diversity over time. To examine primary succession in phototrophic communities, we isolated two unicyanobacterial consortia from the microbial mat in Hot Lake, Washington, characterizing the membership and metabolic function of each consortium. We then analyzed the spatial structures and quantified the community compositions of their assembling biofilms. The consortia retained the same suite of heterotrophic species, identified as abundant members of the mat and assigned to Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes. Autotroph growth rates dominated early in assembly, yielding to increasing heterotroph growth rates late in succession. The two consortia exhibited similar assembly patterns, with increasing relative abundances of members from Bacteroidetes and Alphaproteobacteria concurrent with decreasing relative abundances of those from Gammaproteobacteria. Despite these similarities at higher taxonomic levels, the relative abundances of individual heterotrophic species were substantially different in the developing consortial biofilms. This suggests that, although similar niches are created by the cyanobacterial metabolisms, the resulting webs of autotroph-heterotroph and heterotroph-heterotroph interactions are specific to each primary producer. The relative simplicity and tractability of the Hot Lake unicyanobacterial consortia make them useful model systems for deciphering interspecies interactions and assembly principles relevant to natural microbial communities.

GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data

MOTIVATION

Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed.

RESULTS

GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397.

AVAILABILITY

GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

CONTACTS

brianjamesschmidt@gmail.com

Salmonella utilizes D-glucosaminate via a mannose family phosphotransferase system permease and associated enzymes

Salmonella enterica is a globally significant bacterial food-borne pathogen that utilizes a variety of carbon sources. We report here that Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) uses d-glucosaminate (2-amino-2-deoxy-d-gluconic acid) as a carbon and nitrogen source via a previously uncharacterized mannose family phosphotransferase system (PTS) permease, and we designate the genes encoding the permease dgaABCD (d-glucosaminate PTS permease components EIIA, EIIB, EIIC, and EIID). Two other genes in the dga operon (dgaE and dgaF) were required for wild-type growth of S. Typhimurium with d-glucosaminate. Transcription of dgaABCDEF was dependent on RpoN (σ(54)) and an RpoN-dependent activator gene we designate dgaR. Introduction of a plasmid bearing dgaABCDEF under the control of the lac promoter into Escherichia coli strains DH5α, BL21, and JM101 allowed these strains to grow on minimal medium containing d-glucosaminate as the sole carbon and nitrogen source. Biochemical and genetic data support a catabolic pathway in which d-glucosaminate, as it is transported across the cell membrane, is phosphorylated at the C-6 position by DgaABCD. DgaE converts the resulting d-glucosaminate-6-phosphate to 2-keto-3-deoxygluconate 6-phosphate (KDGP), which is subsequently cleaved by the aldolase DgaF to form glyceraldehyde-3-phosphate and pyruvate. DgaF catalyzes the same reaction as that catalyzed by Eda, a KDGP aldolase in the Entner-Doudoroff pathway, and the two enzymes can substitute for each other in their respective pathways. Examination of the Integrated Microbial Genomes database revealed that orthologs of the dga genes are largely restricted to certain enteric bacteria and a few species in the phylum Firmicutes.