Serum trace metal association with response to erythropoiesis stimulating agents in incident and prevalent hemodialysis patients.

Alterations in hemodialysis patients' serum trace metals have been documented. Early studies addressing associations levels of serum trace metals with erythropoietic responses and/or hematocrit generated mixed results. These studies were conducted prior to current approaches for erythropoiesis stimulating agent (ESA) drug dosing guidelines or without consideration of inflammation markers (e.g. hepcidin) important for regulation of iron availability. This study sought to determine if the serum trace metal concentrations of incident or chronic hemodialysis patients associated with the observed ESA response variability and with consideration to ESA dose response, hepcidin, and high sensitivity C-reactive protein levels. Inductively-coupled plasma-mass spectrometry was used to measure 14 serum trace metals in 29 incident and 79 prevalent dialysis patients recruited prospectively. We compared these data to three measures of ESA dose response, sex, and dialysis incidence versus dialysis prevalence. Hemoglobin was negatively associated with ESA dose and cadmium while positively associated with antimony, arsenic and lead. ESA dose was negatively associated with achieved hemoglobin and vanadium while positively associated with arsenic. ESA response was positively associated with arsenic. Vanadium, nickel, cadmium, and tin were increased in prevalent patients. Manganese was increased in incident patients. Vanadium, nickel, and arsenic increased with time on dialysis while manganese decreased. Changes in vanadium and manganese were largest and appeared to have some effect on anemia. Incident and prevalent patients' chromium and antimony levels exceeded established accepted upper limits of normal.

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

INTRODUCTION: Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS. METHODS: We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance. RESULTS: Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P = 0.003; area under the receiver operating characteristic curve = 0.78). CONCLUSION: Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Metabolomics.

INTRODUCTION: Nephrotic syndrome (NS) is a kidney disease that affects both children and adults. Glucocorticoids have been the primary therapy for >60 years but are ineffective in approximately 20% of children and approximately 50% of adult patients. Unfortunately, patients with steroid-resistant NS (SRNS; vs. steroid-sensitive NS [SSNS]) are at high risk for both glucocorticoid-induced side effects and disease progression. METHODS: We performed proton nuclear magnetic resonance (1H NMR) metabolomic analyses on plasma samples (n = 86) from 45 patients with NS (30 SSNS and 15 SRNS) obtained at initial disease presentation before glucocorticoid initiation and after approximately 7 weeks of glucocorticoid therapy to identify candidate biomarkers able to either predict SRNS before treatment or define critical molecular pathways/targets regulating steroid resistance. RESULTS: Stepwise logistic regression models identified creatinine concentration and glutamine concentration (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.99-1.02) as 2 candidate biomarkers predictive of SRNS, and malonate concentration (OR: 0.94; 95% CI: 0.89-1.00) as a third candidate predictive biomarker using a similar model (only in children >3 years). In addition, paired-sample analyses identified several candidate biomarkers with the potential to identify mechanistic molecular pathways/targets that regulate clinical steroid resistance, including lipoproteins, adipate, pyruvate, creatine, glucose, tyrosine, valine, glutamine, and sn-glycero-3-phosphcholine. CONCLUSION: Metabolomic analyses of serial plasma samples from children with SSNS and SRNS identified elevated creatinine and glutamine concentrations, and reduced malonate concentrations, as auspicious candidate biomarkers to predict SRNS at disease onset in pediatric NS, as well as additional candidate biomarkers with the potential to identify mechanistic molecular pathways that may regulate clinical steroid resistance.

Metabolomics applied to islet nutrient sensing mechanisms.

After multiple decades of investigation, the precise mechanisms involved in fuel-stimulated insulin secretion are still being revealed. One avenue for gaining deeper knowledge is to apply emergent tools of "metabolomics," involving mass spectrometry and nuclear magnetic resonance-based profiling of islet cells in their fuel-stimulated compared with basal states. The current article summarizes recent insights gained from application of metabolomics tools to the specific process of glucose-stimulated insulin secretion, revealing 2 new mechanisms that may provide targets for improving insulin secretion in diabetes.

Metabolic profiling of a chronic kidney disease cohort reveals metabolic phenotype more likely to benefit from a probiotic.

SCOPE: Persistent reduction in Glomerular Filtration Rate (GFR) is a hallmark of Chronic Kidney Disease (CKD) and is associated with an elevation of Blood Urea Nitrogen (BUN). This metabolomics pilot study sought to identify metabolites that differentiated patients with CKD whose BUN decreased on a probiotic and possible mechanisms. METHODS AND RESULTS: Metabolomics was used to analyze baseline plasma samples previously diagnosed with CKD Stage III-IV. Patients had participated in a dose escalation study of the probiotic Renadyl™. A total of 24 samples were categorized depending on whether BUN increased or decreased from baseline after 4 months of probiotic use. Multivariate analysis was used to analyze the data and determine the metabolites that best differentiated the phenotypic groups. The sixteen patients who had a decrease in BUN were not significantly different based on demographic and clinical measures from those whose BUN increased or did not change with the exception of age. Eleven of the fourteen metabolites that differentiated the groups were known to be modulated by gut microflora, which may eventually provide a mechanistic link between probiotic and outcomes. CONCLUSIONS: Metabolomics revealed metabolites at baseline that may predict individuals with CKD that would most benefit from a probiotics.

Metabolomics applied to the pancreatic islet.

Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies.

Adenylosuccinate Is an Insulin Secretagogue Derived from Glucose-Induced Purine Metabolism.

Pancreatic islet failure, involving loss of glucose-stimulated insulin secretion (GSIS) from islet ß cells, heralds the onset of type 2 diabetes (T2D). To search for mediators of GSIS, we performed metabolomics profiling of the insulinoma cell line 832/13 and uncovered significant glucose-induced changes in purine pathway intermediates, including a decrease in inosine monophosphate (IMP) and an increase in adenylosuccinate (S-AMP), suggesting a regulatory role for the enzyme that links the two metabolites, adenylosuccinate synthase (ADSS). Inhibition of ADSS or a more proximal enzyme in the S-AMP biosynthesis pathway, adenylosuccinate lyase, lowers S-AMP levels and impairs GSIS. Addition of S-AMP to the interior of patch-clamped human ß cells amplifies exocytosis, an effect dependent upon expression of sentrin/SUMO-specific protease 1 (SENP1). S-AMP also overcomes the defect in glucose-induced exocytosis in ß cells from a human donor with T2D. S-AMP is, thus, an insulin secretagogue capable of reversing ß cell dysfunction in T2D.

Carnitine Acetyltransferase Mitigates Metabolic Inertia and Muscle Fatigue during Exercise.

Acylcarnitine metabolites have gained attention as biomarkers of nutrient stress, but their physiological relevance and metabolic purpose remain poorly understood. Short-chain carnitine conjugates, including acetylcarnitine, derive from their corresponding acyl-CoA precursors via the action of carnitine acetyltransferase (CrAT), a bidirectional mitochondrial matrix enzyme. We show here that contractile activity reverses acetylcarnitine flux in muscle, from net production and efflux at rest to net uptake and consumption during exercise. Disruption of this switch in mice with muscle-specific CrAT deficiency resulted in acetyl-CoA deficit, perturbed energy charge, and diminished exercise tolerance, whereas acetylcarnitine supplementation produced opposite outcomes in a CrAT-dependent manner. Likewise, in exercise-trained compared to untrained humans, post-exercise phosphocreatine recovery rates were positively associated with CrAT activity and coincided with dramatic shifts in muscle acetylcarnitine dynamics. These findings show acetylcarnitine serves as a critical acetyl buffer for working muscles and provide insight into potential therapeutic strategies for combatting exercise intolerance.

Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition.

Viruses contribute to the mortality of marine microbes, consequentially altering biological species composition and system biogeochemistry. Although it is well established that host cells provide metabolic resources for virus replication, the extent to which infection reshapes host metabolism at a global level and the effect of this alteration on the cellular material released following viral lysis is less understood. To address this knowledge gap, the growth dynamics, metabolism and extracellular lysate of roseophage-infected Sulfitobacter sp. 2047 was studied using a variety of techniques, including liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. Quantitative estimates of the total amount of carbon and nitrogen sequestered into particulate biomass indicate that phage infection redirects ∼75% of nutrients into virions. Intracellular concentrations for 82 metabolites were measured at seven time points over the infection cycle. By the end of this period, 71% of the detected metabolites were significantly elevated in infected populations, and stable isotope-based flux measurements showed that these cells had elevated metabolic activity. In contrast to simple hypothetical models that assume that extracellular compounds increase because of lysis, a profile of metabolites from infected cultures showed that >70% of the 56 quantified compounds had decreased concentrations in the lysate relative to uninfected controls, suggesting that these small, labile nutrients were being utilized by surviving cells. These results indicate that virus-infected cells are physiologically distinct from their uninfected counterparts, which has implications for microbial community ecology and biogeochemistry.

Integration of the second messenger c-di-GMP into the chemotactic signaling pathway.

UNLABELLED: Elevated intracellular levels of the bacterial second messenger c-di-GMP are known to suppress motility and promote sessility. Bacterial chemotaxis guides motile cells in gradients of attractants and repellents over broad concentration ranges, thus allowing bacteria to quickly adapt to changes in their surroundings. Here, we describe a chemotaxis receptor that enhances, as opposed to suppresses, motility in response to temporary increases in intracellular c-di-GMP. Azospirillum brasilense's preferred metabolism is adapted to microaerophily, and these motile cells quickly navigate to zones of low oxygen concentration by aerotaxis. We observed that changes in oxygen concentration result in rapid changes in intracellular c-di-GMP levels. The aerotaxis and chemotaxis receptor, Tlp1, binds c-di-GMP via its C-terminal PilZ domain and promotes persistent motility by increasing swimming velocity and decreasing swimming reversal frequency, which helps A. brasilense reach low-oxygen zones. If c-di-GMP levels remain high for extended periods, A. brasilense forms nonmotile clumps or biofilms on abiotic surfaces. These results suggest that association of increased c-di-GMP levels with sessility is correct on a long-term scale, while in the short-term c-di-GMP may actually promote, as opposed to suppress, motility. Our data suggest that sensing c-di-GMP by Tlp1 functions similar to methylation-based adaptation. Numerous chemotaxis receptors contain C-terminal PilZ domains or other sensory domains, suggesting that intracellular c-di-GMP as well as additional stimuli can be used to modulate adaptation of bacterial chemotaxis receptors. IMPORTANCE: To adapt and compete under changing conditions, bacteria must not only detect and respond to various environmental cues but also be able to remain sensitive to further changes in the environmental conditions. In bacterial chemotaxis, chemosensory sensitivity is typically brought about by changes in the methylation status of chemotaxis receptors capable of modulating the ability of motile cells to navigate in gradients of various physicochemical cues. Here, we show that the ubiquitous second messenger c-di-GMP functions to modulate chemosensory sensitivity of a bacterial chemotaxis receptor in the alphaproteobacterium Azospirillum brasilense. Binding of c-di-GMP to the chemotaxis receptor promotes motility under conditions of elevated intracellular c-di-GMP levels. Our results revealed that the role of c-di-GMP as a sessile signal is overly simplistic. We also show that adaptation by sensing an intracellular metabolic cue, via PilZ or other domains, is likely widespread among bacterial chemotaxis receptors.

MetabR: an R script for linear model analysis of quantitative metabolomic data.

BACKGROUND: Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. FINDINGS: Here we present a simple menu-driven program, "MetabR", designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. CONCLUSIONS: We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org/.

Transcriptomic and metabolomic profiling of chicken adipose tissue in response to insulin neutralization and fasting.

BACKGROUND: Domestic broiler chickens rapidly accumulate adipose tissue due to intensive genetic selection for rapid growth and are naturally hyperglycemic and insulin resistant, making them an attractive addition to the suite of rodent models used for studies of obesity and type 2 diabetes in humans. Furthermore, chicken adipose tissue is considered as poorly sensitive to insulin and lipolysis is under glucagon control. Excessive fat accumulation is also an economic and environmental concern for the broiler industry due to the loss of feed efficiency and excessive nitrogen wasting, as well as a negative trait for consumers who are increasingly conscious of dietary fat intake. Understanding the control of avian adipose tissue metabolism would both enhance the utility of chicken as a model organism for human obesity and insulin resistance and highlight new approaches to reduce fat deposition in commercial chickens. RESULTS: We combined transcriptomics and metabolomics to characterize the response of chicken adipose tissue to two energy manipulations, fasting and insulin deprivation in the fed state. Sixteen to 17 day-old commercial broiler chickens (ISA915) were fed ad libitum, fasted for five hours, or fed but deprived of insulin by injections of anti-insulin serum. Pair-wise contrasts of expression data identified a total of 2016 genes that were differentially expressed after correction for multiple testing, with the vast majority of differences due to fasting (1780 genes). Gene Ontology and KEGG pathway analyses indicated that a short term fast impacted expression of genes in a broad selection of pathways related to metabolism, signaling and adipogenesis. The effects of insulin neutralization largely overlapped with the response to fasting, but with more modest effects on adipose tissue metabolism. Tissue metabolomics indicated unique effects of insulin on amino acid metabolism. CONCLUSIONS: Collectively, these data provide a foundation for further study into the molecular basis for adipose expansion in commercial poultry and identify potential pathways through which fat accretion may be attenuated in the future through genetic selection or management practices. They also highlight chicken as a useful model organism in which to study the dynamic relationship between food intake, metabolism, and adipose tissue biology.

Riboneogenesis in yeast.

Glucose is catabolized in yeast via two fundamental routes, glycolysis and the oxidative pentose phosphate pathway, which produces NADPH and the essential nucleotide component ribose-5-phosphate. Here, we describe riboneogenesis, a thermodynamically driven pathway that converts glycolytic intermediates into ribose-5-phosphate without production of NADPH. Riboneogenesis begins with synthesis, by the combined action of transketolase and aldolase, of the seven-carbon bisphosphorylated sugar sedoheptulose-1,7-bisphosphate. In the pathway's committed step, sedoheptulose bisphosphate is hydrolyzed to sedoheptulose-7-phosphate by the enzyme sedoheptulose-1,7-bisphosphatase (SHB17), whose activity we identified based on metabolomic analysis of the corresponding knockout strain. The crystal structure of Shb17 in complex with sedoheptulose-1,7-bisphosphate reveals that the substrate binds in the closed furan form in the active site. Sedoheptulose-7-phosphate is ultimately converted by known enzymes of the nonoxidative pentose phosphate pathway to ribose-5-phosphate. Flux through SHB17 increases when ribose demand is high relative to demand for NADPH, including during ribosome biogenesis in metabolically synchronized yeast cells.

Two-dimensional liquid chromatography/mass spectrometry/mass spectrometry separation of water-soluble metabolites.

Off-line two-dimensional liquid chromatography with tandem mass spectrometry detection (2D-LC/MS-MS) was used to separate a set of metabolomic species. Water-soluble metabolites were extracted from Escherichia coli and Saccharomyces cerevisae cultures and were immediately analyzed using strong cation exchange (SCX)-hydrophilic interaction chromatography (HILIC). Metabolite mixtures are well-suited for multidimensional chromatography as the range of components varies widely with respect to polarity and chemical makeup. Some currently used methods employ two different separations for the detection of positively and negatively ionized metabolites by mass spectrometry. Here we developed a single set of chromatographic conditions for both ionization modes and were able to detect a total of 141 extracted metabolite species, with an overall peak capacity of ca. 2500. We show that a single two-dimensional separation method is sufficient and practical when a pair or more of unidimensional separations are used in metabolomics.

Establishing a quantitative definition of quorum sensing provides insight into the information content of the autoinducer signals in Vibrio harveyi and Escherichia coli.

Extracellular autoinducer concentrations in cultures of Vibrio harveyi and Escherichia coli were monitored by liquid chromatography-tandem mass spectrometry to test whether a quantitative definition of quorum sensing could help decipher the information content of these signals. Although V. harveyi was able to keep the autoinducer-2 to cell number ratio constant, the ratio of signal to cell number for V. harveyi autoinducer-1 and E. coli autoinducer-2 varied as the cultures grew. These data indicate that V. harveyi uses autoinducer-2 for quorum sensing, while the other molecules may be used to transmit different information or are influenced by metabolic noise.

Direct quantitation of the quorum sensing signal, autoinducer-2, in clinically relevant samples by liquid chromatography-tandem mass spectrometry.

Quorum Sensing is a type of bacterial cell-to-cell signaling that allows for cell density dependent regulation of gene expression. Many of the behaviors mediated by quorum sensing are critical for bacterial colonization or infection, and autoinducer-2 has been proposed as a universal interspecies signaling molecule that allows multispecies colonies of bacteria, e.g., biofilms or dental plaque, to behave as pseudomulticellular organisms. However, the direct detection of autoinducer-2 has been difficult, leaving the in vivo relevance of this signal in question. Herein we report a liquid chromatography-tandem mass spectrometric technique that enables reproducible, quantitative, and sensitive measurement of the concentration of autoinducer-2 from a variety of sources. This technique was applied to the detection of autoinducer-2 from Escherichia coli and Vibrio harveyi in proof-of-concept studies and was then used to directly measure the concentration of the signal produced by oral bacteria in human saliva.