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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.

Abstract 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/.

Abstract 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.

Liquid chromatography-tandem mass spectrometry was used to monitor the e xtracellular autoinducer concentrations in cultures of Vibrio harveyi and Escherichia coli and determine if a quantitative definition of quorum sensing could provide insight into the information content of autoinducer signals. The data obtained revealed that V. harveyi uses autoinducer-2 for quorum sensing, while the other molecules might be used to transmit different information or might be influenced by metabolic noise.

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. [ABSTRACT FROM AUTHOR]

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). Tins metabolomics pilot study sought to identify metabolites that differentiated patients with CKD whose BUN decreased on a probiotic and possible associated mechanisms. Metabolomics was used to analyze baseline plasma samples from subjects previously diagnosed with CKD Stage III-TV. Patients had participated in a dose escalation study of the probiotic RenadyFM. A total of 24 samples were categorized depending on whether BUN increased or decreasedfrom 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. Metabolomics revealed metabolites at baseline that may predict individuals with CKD that would most benefit from probiotics. [ABSTRACT FROM AUTHOR]

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., (© 2019 International Society of Nephrology. Published by Elsevier Inc.)

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 ( 1 H 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., (© 2019 International Society of Nephrology. Published by Elsevier Inc.)