PubMed

Covalent attachment and Pro-Pro endopeptidase (PPEP-1)-mediated release of Clostridium difficile cell surface proteins involved in adhesion. Mol Microbiol. 2017 Jun 21;: Authors: Corver J, Cordo' V, van Leeuwen HC, Klychnikov OI, Hensbergen PJ Abstract In the past decade, Clostridium difficile has emerged as an important gut pathogen. This anaerobic, Gram-positive bacterium is the main cause of infectious nosocomial diarrhea. Whereas much is known about the mechanism through which the C. difficile toxins cause diarrhea, relatively little is known about the dynamics of adhesion and motility, which is mediated by cell surface proteins. This review will discuss the recent advances in our understanding of the sortase-mediated covalent attachment of cell surface (adhesion) proteins to the peptidoglycan layer of C. difficile and their release through the action of a highly specific secreted metalloprotease (Pro-Pro endopeptidase 1, PPEP-1). Specific emphasis will be on a model in which PPEP-1 and its substrates control the switch from a sessile to motile phenotype in C. difficile, and how this is regulated by the cyclic dinucleotide c-di-GMP (3'-5' cyclic dimeric guanosine monophosphate). This article is protected by copyright. All rights reserved. PMID: 28636257 [PubMed - as supplied by publisher]

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics. Metabolites. 2017 Jun 21;7(2): Authors: Trainor PJ, DeFilippis AP, Rai SN Abstract Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed. PMID: 28635678 [PubMed - in process]

The protein Slr1143 is an active diguanylate cyclase in Synechocystis sp. PCC 6803 and interacts with the photoreceptor Cph2. Microbiology. 2017 Jun 21;: Authors: Angerer V, Schwenk P, Wallner T, Kaever V, Hiltbrunner A, Wilde A Abstract Cyclic-di-GMP is an ubiquitous second messenger in bacteria. Several c-di-GMP receptor proteins have been identified to date, and downstream signalling pathways are often mediated through protein-protein interactions. The photoreceptor Cph2 from the cyanobacterium Synechocystis sp. PCC 6803 comprises three domains related to c-di-GMP metabolism: two GGDEF and one EAL domain. It has been shown that the C-terminal GGDEF domain acts as blue-light triggered c-di-GMP producer thereby inhibiting motility of the cells in blue light. The specific function of the other two c-di-GMP related domains remained unclear. In this study, we test knockout mutants of potential interaction partners of Cph2 for altered phototactic behaviour. Whereas wild-type cells are non-motile under high-intensity red light of 640 nm, the mutant Δslr1143 displays positive phototaxis. This phenotype can be complemented by overexpression of full-length Slr1143, which also results in an increased cellular c-di-GMP concentration. However, the non-motile phenotype of wild-type cells under high-intensity red light appears not to be due to an elevated cellular c-di-GMP content. Using co-precipitation and yeast two-hybrid assays, we demonstrate that the GGDEF domain of Slr1143 interacts with the EAL and the GGDEF domains of Cph2. However, under the test conditions, the interaction of the two proteins is not light-dependent. We conclude that Slr1143 is a new Cph2-interacting regulatory factor which modulates motility under red light and accordingly we propose Cip1 (Cph2-interacting protein 1) as a new designation for this gene product. PMID: 28635593 [PubMed - as supplied by publisher]

Anti-adhesive Activity and Metabolomics Analysis of Rat Urine After Cranberry (Vaccinium macrocarpon Aiton) Administration. J Agric Food Chem. 2017 Jun 21;: Authors: Peron G, Anna P, Paola B, Schievano E, Mammi S, Sut S, Castagliuolo I, Dall'Acqua S Abstract Cranberry (Vaccinium macrocarpon Aiton) is used to treat non-complicated urinary tract infections (UTIs). A-type procyanidins (PAC-A) are considered the active constituents being able to inhibit bacterial adhesion to the urinary epithelium. However, there are doubts about PAC-A role in UTIs, due to their poor bioavailability, extensive metabolism, limited knowledge about urinary excretion and contradictory clinical trials. The effects of 35 days cranberry (11 mg/kg PAC-A, 4 mg/kg PAC-B) supplementation were studied in healthy rats using a UPLC-MS-based metabolomics approach. Microbial PACs metabolites, as valeric acid and valerolactone derivatives, were related to cranberry consumption. An increased urinary excretion of glucuronidated metabolites was also observed. In a further experiment, urine samples were collected at 2, 4, 8 and 24 hours after cranberry intake and their anti-adhesive properties were tested against uropathogenic Escherichia coli. The 8 hours samples presented the highest activity. Changes in urinary composition were studied by UPLC-QTOF, observing the presence of PACs metabolites. Furthermore, PAC-A2 levels were measured in all collected samples, allowing the detection of ng/mL levels in the 4 hours ones, being the highest amounts found. Results indicate that anti-adhesive activity against uropathogenic bacteria observed after cranberry consumption is ascribable to PAC-A metabolites rather than to direct PAC-A effect, being the measured PAC-A levels in urine lower than those reported as active in literature. PMID: 28635280 [PubMed - as supplied by publisher]

Related Articles Metabolomic Analysis and Mode of Action of Metabolites of Tea Tree Oil Involved in the Suppression of Botrytis cinerea. Front Microbiol. 2017;8:1017 Authors: Xu J, Shao X, Li Y, Wei Y, Xu F, Wang H Abstract Tea tree oil (TTO), a volatile essential oil, has been widely used as an antimicrobial agent. However, the mechanism underlying TTO antifungal activity is not fully understood. In this study, a comprehensive metabolomics survey was undertaken to identify changes in metabolite production in Botrytis cinerea cells treated with TTO. Significant differences in 91 metabolites were observed, including 8 upregulated and 83 downregulated metabolites in TTO-treated cells. The results indicate that TTO inhibits primary metabolic pathways through the suppression of the tricarboxylic acid (TCA) cycle and fatty acid metabolism. Further experiments show that TTO treatment decreases the activities of key enzymes in the TCA cycle and increases the level of hydrogen peroxide (H2O2). Membrane damage is also induced by TTO treatment. We hypothesize that the effect of TTO on B. cinerea is achieved mainly by disruption of the TCA cycle and fatty acid metabolism, resulting in mitochondrial dysfunction and oxidative stress. PMID: 28634477 [PubMed - in process]

Related Articles Fetal metabolic stress disrupts immune homeostasis and induces pro-inflammatory responses in HIV-1 and cART-exposed infants. J Infect Dis. 2017 Jun 17;: Authors: Schoeman JC, Moutloatse GP, Harms AC, Vreeken RJ, Scherpbier HJ, Van Leeuwen L, Kuijpers TW, Reinecke CJ, Berger R, Hankemeier T, Bunders MJ Abstract Increased morbidity and fetal growth restriction are reported in uninfected children born to HIV-1-infected women treated with antiretroviral (ARV) therapy. Viruses and/or pharmacological interventions such as ARVs can induce metabolic stress skewing the cell's immune response and restricting (cell) growth. Novel metabolomic techniques provided the opportunity to investigate the impact of fetal HIV-1 and combination ARV therapy (cART)-exposure on the infants' immune-metabolome. Peroxidized lipids, generated by reactive oxygen species, were increased in cART/HIV-1-exposed infants, indicating altered mitochondrial functioning. The lipid metabolism was further dysregulated with increased triglyceride species and a subsequent decrease in phospholipids in cART/HIV-1-exposed infants compared to control infants. Pro-inflammatory immune mediators, lysophospholipids as well as cytokines such as CXCL10 and CCL3 were increased while anti-inflammatory metabolites from cytochrome P450 pathway were reduced in cART/HIV-1-exposed infants. Taken together, these data demonstrate that the fetal metabolism is impacted by maternal factors (cART and HIV-1) and skews physiological immune responses towards inflammation in the newborn infant. PMID: 28633455 [PubMed - as supplied by publisher]

Related Articles Neonatal Metabolomic Profiles Related to Prenatal Arsenic Exposure. Environ Sci Technol. 2017 Jan 03;51(1):625-633 Authors: Laine JE, Bailey KA, Olshan AF, Smeester L, Drobná Z, Stýblo M, Douillet C, García-Vargas G, Rubio-Andrade M, Pathmasiri W, McRitchie S, Sumner SJ, Fry RC Abstract Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in Gómez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and %dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure. PMID: 27997141 [PubMed - indexed for MEDLINE]

Related Articles Current LC-MS methods and procedures applied to the identification of new steroid metabolites. J Steroid Biochem Mol Biol. 2016 Sep;162:41-56 Authors: Marcos J, Pozo OJ Abstract The study of the metabolism of steroids has a long history; from the first characterizations of the major metabolites of steroidal hormones in the pre-chromatographic era, to the latest discoveries of new forms of excretions. The introduction of mass spectrometers coupled to gas chromatography at the end of the 1960's represented a major breakthrough for the elucidation of new metabolites. In the last two decades, this technique is being complemented by the use of liquid chromatography-mass spectrometry (LC-MS). In addition of becoming fundamental in clinical steroid determinations due to its excellent specificity, throughput and sensitivity, LC-MS has emerged as an exceptional tool for the discovery of new steroid metabolites. The aim of the present review is to provide an overview of the current LC-MS procedures used in the quest of novel metabolic products of steroidal hormones and exogenous steroids. Several aspects regarding LC separations are first outlined, followed by a description of the key processes that take place in the mass spectrometric analysis, i.e. the ionization of the steroids in the source and the fragmentation of the selected precursor ions in the collision cell. The different analyzers and approaches employed together with representative examples of each of them are described. Special emphasis is placed on triple quadrupole analyzers (LC-MS/MS), since they are the most commonly employed. Examples on the use of precursor ion scan, neutral loss scan and theoretical selected reaction monitoring strategies are also explained. PMID: 26709140 [PubMed - indexed for MEDLINE]

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/21PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

25 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/20PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

A Small Molecule Solution to the Vexing Problem of Restenosis: Predicting Restenosis. JACC Cardiovasc Interv. 2017 Jun 07;: Authors: Quyyumi AA, Samman Tahhan A, Jones DP PMID: 28624382 [PubMed - as supplied by publisher]

Plasma Phospholipids and Sphingolipids Identify Stent Restenosis After Percutaneous Coronary Intervention. JACC Cardiovasc Interv. 2017 Jun 07;: Authors: Cui S, Li K, Ang L, Liu J, Cui L, Song X, Lv S, Mahmud E Abstract OBJECTIVES: The aim of this study was to evaluate the diagnostic utility of plasma metabolomic biomarkers for in-stent restenosis (ISR). BACKGROUND: ISR remains an issue for patients after percutaneous coronary intervention. Identification of biomarkers to predict ISR could be invaluable for patient care. METHODS: Next-generation metabolomic profiling was performed in the discovery phase from the plasma of 400 patients undergoing percutaneous coronary intervention. In the validation phase, targeted analysis was conducted using stable isotope dilution-multiple reaction monitoring mass spectrometry in another independent group of 500 participants. RESULTS: A set of 6 plasma metabolites was discovered and validated for the diagnosis of ISR as early as 1 month after percutaneous coronary intervention. This biomarker panel classified patients with ISR and control subjects with sensitivity of 91% and specificity of 90% in the discovery phase. The diagnostic accuracy in the independent validation phase was 90% (95% confidence interval: 87% to 100%). The defined 6 metabolites all belong to sphingolipid and phospholipid metabolism, including phosphatidylcholine diacyl C36:0, phosphatidylcholine diacyl C34:2, phosphatidylinositol diacyl C36:4, phosphatidic acid C34:1, ceramide, and sphingomyelin diacyl 18:1/20:1. These biomarkers play essential roles in cell signaling that regulates the proliferation and migration of vascular smooth muscle cells. CONCLUSIONS: Next-generation metabolomics demonstrates powerful diagnostic value in estimating ISR-related metabolic disturbance. The defined plasma biomarkers provide better early diagnostic value compared with conventional imaging techniques. PMID: 28624380 [PubMed - as supplied by publisher]

Differential urinary metabolites related with the severity of major depressive disorder. Behav Brain Res. 2017 Jun 14;: Authors: Chen JJ, Zhou CJ, Zheng P, Cheng K, Wang HY, Li J, Zeng L, Xie P Abstract Major depressive disorder (MDD) is a common mental disorder that affects a person's general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC-MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD-specific panel (N-Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD-specific panel (indoxyl sulphate, Taurine, Citrate, 3-hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD. PMID: 28624318 [PubMed - as supplied by publisher]

Importance of optimizing chromatographic conditions and mass spectrometric parameters for supercritical fluid chromatography/mass spectrometry. J Chromatogr A. 2017 Jun 02;: Authors: Fujito Y, Hayakawa Y, Izumi Y, Bamba T Abstract Supercritical fluid chromatography/mass spectrometry (SFC/MS) has great potential in high-throughput and the simultaneous analysis of a wide variety of compounds, and it has been widely used in recent years. The use of MS for detection provides the advantages of high sensitivity and high selectivity. However, the sensitivity of MS detection depends on the chromatographic conditions and MS parameters. Thus, optimization of MS parameters corresponding to the SFC condition is mandatory for maximizing performance when connecting SFC to MS. The aim of this study was to reveal a way to decide the optimum composition of the mobile phase and the flow rate of the make-up solvent for MS detection in a wide range of compounds. Additionally, we also showed the basic concept for determination of the optimum values of the MS parameters focusing on the MS detection sensitivity in SFC/MS analysis. To verify the versatility of these findings, a total of 441 pesticides with a wide polarity range (logPow from -4.21 to 7.70) and pKa (acidic, neutral and basic). In this study, a new SFC-MS interface was used, which can transfer the entire volume of eluate into the MS by directly coupling the SFC with the MS. This enabled us to compare the sensitivity or optimum MS parameters for MS detection between LC/MS and SFC/MS for the same sample volume introduced into the MS. As a result, it was found that the optimum values of some MS parameters were completely different from those of LC/MS, and that SFC/MS-specific optimization of the analytical conditions is required. Lastly, we evaluated the sensitivity of SFC/MS using fully optimized analytical conditions. As a result, we confirmed that SFC/MS showed much higher sensitivity than LC/MS when the analytical conditions were fully optimized for SFC/MS; and the high sensitivity also increase the number of the compounds that can be detected with good repeatability in real sample analysis. This result indicates that SFC/MS has potential for practical use in the multiresidue analysis of a wide range of compounds that requires high sensitivity. PMID: 28624150 [PubMed - as supplied by publisher]

One-carbon metabolism, cognitive impairment and CSF measures of Alzheimer pathology: homocysteine and beyond. Alzheimers Res Ther. 2017 Jun 17;9(1):43 Authors: Dayon L, Guiraud SP, Corthésy J, Da Silva L, Migliavacca E, Tautvydaitė D, Oikonomidi A, Moullet B, Henry H, Métairon S, Marquis J, Descombes P, Collino S, Martin FJ, Montoliu I, Kussmann M, Wojcik J, Bowman GL, Popp J Abstract BACKGROUND: Hyperhomocysteinemia is a risk factor for cognitive decline and dementia, including Alzheimer disease (AD). Homocysteine (Hcy) is a sulfur-containing amino acid and metabolite of the methionine pathway. The interrelated methionine, purine, and thymidylate cycles constitute the one-carbon metabolism that plays a critical role in the synthesis of DNA, neurotransmitters, phospholipids, and myelin. In this study, we tested the hypothesis that one-carbon metabolites beyond Hcy are relevant to cognitive function and cerebrospinal fluid (CSF) measures of AD pathology in older adults. METHODS: Cross-sectional analysis was performed on matched CSF and plasma collected from 120 older community-dwelling adults with (n = 72) or without (n = 48) cognitive impairment. Liquid chromatography-mass spectrometry was performed to quantify one-carbon metabolites and their cofactors. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to clinical and biomarker measures that generate the highest diagnostic accuracy of a priori-defined cognitive impairment (Clinical Dementia Rating-based) and AD pathology (i.e., CSF tau phosphorylated at threonine 181 [p-tau181]/β-Amyloid 1-42 peptide chain [Aβ1-42] >0.0779) to establish a reference benchmark. Two other LASSO-determined models were generated that included the one-carbon metabolites in CSF and then plasma. Correlations of CSF and plasma one-carbon metabolites with CSF amyloid and tau were explored. LASSO-determined models were stratified by apolipoprotein E (APOE) ε4 carrier status. RESULTS: The diagnostic accuracy of cognitive impairment for the reference model was 80.8% and included age, years of education, Aβ1-42, tau, and p-tau181. A model including CSF cystathionine, methionine, S-adenosyl-L-homocysteine (SAH), S-adenosylmethionine (SAM), serine, cysteine, and 5-methyltetrahydrofolate (5-MTHF) improved the diagnostic accuracy to 87.4%. A second model derived from plasma included cystathionine, glycine, methionine, SAH, SAM, serine, cysteine, and Hcy and reached a diagnostic accuracy of 87.5%. CSF SAH and 5-MTHF were associated with CSF tau and p-tau181. Plasma one-carbon metabolites were able to diagnose subjects with a positive CSF profile of AD pathology in APOE ε4 carriers. CONCLUSIONS: We observed significant improvements in the prediction of cognitive impairment by adding one-carbon metabolites. This is partially explained by associations with CSF tau and p-tau181, suggesting a role for one-carbon metabolism in the aggregation of tau and neuronal injury. These metabolites may be particularly critical in APOE ε4 carriers. PMID: 28623948 [PubMed - in process]

25 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/18PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

19 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/16PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/15PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2017/06/14PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample. J Vis Exp. 2017 Jun 01;(124): Authors: Salem M, Bernach M, Bajdzienko K, Giavalisco P Abstract Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures. PMID: 28605387 [PubMed - in process]