PubMed

Related Articles Association of atopic dermatitis with cardiovascular risk factors and diseases. J Invest Dermatol. 2016 Dec 20;: Authors: Standl M, Tesch F, Baurecht H, Rodríguez E, Müller-Nurasyid M, Gieger C, Peters A, Wang-Sattler R, Prehn C, Adamski J, Kronenberg F, Schulz H, Koletzko S, Schikowski T, von Berg A, Lehmann I, Berdel D, Heinrich J, Schmitt J, Weidinger S Abstract Epidemiological studies suggested an association between atopic dermatitis (AD) and cardiovascular disease (CVD). Therefore, we investigate associations and potential underlying pathways of AD and CVD in large cohort studies: the AOK PLUS cohort (n=1.2Mio), the GINIplus/LISAplus birth cohorts (n=2286), and the KORA F4 cohort (n=2990). Additionally, metabolomics in KORA F4 and established cardiovascular risk loci in genome-wide data on 10,788 AD cases and 30,047 controls were analyzed. Longitudinal analysis of AD patients in AOK PLUS showed slightly increased risk for incident angina pectoris (AP) (adjusted risk ratio 1.17; 95%-confidence interval 1.12-1.23), hypertension (1.04 (1.02-1.06)) and peripheral arterial disease (PAD) (1.15 (1.11-1.19)) but not for myocardial infarction (MI) (1.05 (0.99-1.12) and stroke (1.02 (0.98-1.07)). In KORA F4 and GINIplus/LISAplus, AD was not associated with cardiovascular risk factors (CVRFs) and no differences in metabolite levels were detected. There was no robust evidence for shared genetic risk variants of AD and CVD. This study indicates only a marginally increased risk for AP, hypertension and PAD and no increased risk for MI or stroke in AD patients. Relevant associations of AD with CVRFs reported in US-populations could not be confirmed. Likewise, AD patients did not have increased genetic risk factors for CVD. PMID: 28011146 [PubMed - as supplied by publisher]

Related Articles An in-source multiple collision-neutral loss filtering based nontargeted metabolomics approach for the comprehensive analysis of malonyl-ginsenosides from Panax ginseng, P. quinquefolius, and P. notoginseng. Anal Chim Acta. 2017 Feb 01;952:59-70 Authors: Shi XJ, Yang WZ, Qiu S, Yao CL, Shen Y, Pan HQ, Bi QR, Yang M, Wu WY, Guo DA Abstract The simultaneous identification and quantification of target metabolites from herbal medicines are difficult to implement by the full-scan MS based nontargeted metabolomics approaches. Here an in-source multiple collision-neutral loss filtering (IMC-NLF) based nontargeted metabolomics approach is developed and applied to identify and quantify the variations of malonyl-ginsenosides, a common group of acyl saponins with potential anti-diabetic activity, among Panax ginseng, P. quinquefolius, and P. notoginseng. The key steps of the IMC-NLF strategy are the acquisition of specific high-resolution neutral loss data and the efficient filtering of target precursor ions from the full-scan spectra. Using a hybrid LTQ-Orbitrap mass spectrometer after UHPLC separation, abundant in-source product ions, [M-H-CO2](-) (due to the vulnerability of the carboxyl group) and [M-H-Mal.](-), were generated at the energies of 70 V and 90 V, respectively. After spectral deconvolution, the generated peak list was screened by dual NLF using a Neutral Loss MS Finder software (NL of 43.9898 Da for CO2 and 86.0004 Da for the malonyl substituent). By combining the precursor ions list-triggered HCD-MS/MS and basic hydrolysis, a total of 101 malonyl-ginsenosides (including 69 from P. ginseng, 52 from P. quinquefolius, and 44 from P. notoginseng) were identified or tentatively characterized. The variations of 81 characterized malonyl-ginsenosides among 45 batches of Ginseng samples were statistically analyzed disclosing ten potential markers. It is the first systematic analysis of malonyl-ginsenosides. The IMC-NLF approach by a single analytical platform is promising in targeted analyses of modification-specific metabolites in metabolomics and drug metabolism. PMID: 28010843 [PubMed - in process]

Related Articles Integrated work-flow for quantitative metabolome profiling of plants, Peucedani Radix as a case. Anal Chim Acta. 2017 Feb 08;953:40-47 Authors: Song Y, Song Q, Liu Y, Li J, Wan JB, Wang Y, Jiang Y, Tu P Abstract Universal acquisition of reliable information regarding the qualitative and quantitative properties of complicated matrices is the premise for the success of metabolomics study. Liquid chromatography-mass spectrometry (LC-MS) is now serving as a workhorse for metabolomics; however, LC-MS-based non-targeted metabolomics is suffering from some shortcomings, even some cutting-edge techniques have been introduced. Aiming to tackle, to some extent, the drawbacks of the conventional approaches, such as redundant information, detector saturation, low sensitivity, and inconstant signal number among different runs, herein, a novel and flexible work-flow consisting of three progressive steps was proposed to profile in depth the quantitative metabolome of plants. The roots of Peucedanum praeruptorum Dunn (Peucedani Radix, PR) that are rich in various coumarin isomers, were employed as a case study to verify the applicability. First, offline two dimensional LC-MS was utilized for in-depth detection of metabolites in a pooled PR extract namely universal metabolome standard (UMS). Second, mass fragmentation rules, notably concerning angular-type pyranocoumarins that are the primary chemical homologues in PR, and available databases were integrated for signal assignment and structural annotation. Third, optimum collision energy (OCE) as well as ion transition for multiple monitoring reaction measurement was online optimized with a reference compound-free strategy for each annotated component and large-scale relative quantification of all annotated components was accomplished by plotting calibration curves via serially diluting UMS. It is worthwhile to highlight that the potential of OCE for isomer discrimination was described and the linearity ranges of those primary ingredients were extended by suppressing their responses. The integrated workflow is expected to be qualified as a promising pipeline to clarify the quantitative metabolome of plants because it could not only holistically provide qualitative information, but also straightforwardly generate accurate quantitative dataset. PMID: 28010741 [PubMed - in process]

Related Articles An efficient data filtering strategy for easy metabolite detection from the direct analysis of a biological fluid using Fourier transform mass spectrometry. Rapid Commun Mass Spectrom. 2016 Dec 23;: Authors: Rathahao-Paris E, Alves S, Debrauwer L, Cravedi JP, Paris A Abstract RATIONALE: High throughput analyses require an overall analytical workflow including robust and high speed technical platform but also dedicated data processing tools able to extract the relevant information. This work aimed at evaluating post-acquisition data mining tools for selective extraction of metabolite species from direct introduction high resolution mass spectrometry data. METHODS: Investigations were performed on spectral data in which seven metabolites of vinclozolin, a dicarboximide fungicide containing two chloride atoms, were previously manually identified. The spectral data obtained from direct introduction (DI) and high resolution mass spectrometry (HRMS) detection were post-processed by plotting the mass defect profiles and applying various data filtering methods based on accurate mass values. RESULTS: Exploration of mass defect profiles highlighted, in a specific plotting region the presence of compounds containing common chemical elements and pairs of conjugated and non-conjugated metabolites resulting from classical metabolic pathways. Additionally, the judicious application of mass defect and/or isotope pattern filters removed many interfering ions from DI-HRMS data, greatly facilitating the detection of vinclozolin metabolites. Compared to previous results obtained by manual data treatment, three additional metabolites of vinclozolin were detected and putatively annotated. CONCLUSIONS: Tracking simultaneously several specific species could be efficiently performed using data mining tools based on accurate mass values. The selectivity of the data extraction was improved when the isotope filter was used for halogenated compounds, facilitating metabolite ion detection even for low abundance species. This article is protected by copyright. All rights reserved. PMID: 28010043 [PubMed - as supplied by publisher]

Related Articles Mass spectrometry-driven drug discovery for development of herbal medicine. Mass Spectrom Rev. 2016 Dec 23;: Authors: Zhang A, Sun H, Wang X Abstract Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. PMID: 28009933 [PubMed - as supplied by publisher]

Related Articles Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis. Metabolites. 2016 Dec 21;6(4): Authors: Bakalov V, Amathieu R, Triba MN, Clément MJ, Reyes Uribe L, Le Moyec L, Kaynar AM Abstract Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate. PMID: 28009836 [PubMed]

Related Articles Metabolic profiles revealed synergistically antidepressant effects of Lilies and Rhizoma Anemarrhenae in a rat model of depression. Biomed Chromatogr. 2016 Dec 23;: Authors: Du H, Zhao H, Lai X, Lin Q, Zhu Z, Chai Y, Lou Z Abstract Depression is the predominant cause of illness and disability. We applied untargeted metabolomics using mass spectrometry to identify metabolic signatures associated with depression in serum and explored the antidepressant effects of Lilies and Rhizoma Anemarrhenae on an experimental model of chronic unpredictable mild stress (CUMS). Meanwhile metabolomics based on UHPLC-Q-TOF-MS, was used to study the change in metabolites in CUMS rat serum and to evaluate the effects of Anemarrhena Rhizoma, Lilies (alone and in combination). Partial least squares-discriminant analysis identified thirty metabolites as decisive marker compounds that discriminated the CUMS rats and the control rats. The majority of these metabolites were involved in amino acid metabolism, the tricarboxylic acid cycle, and phosphoglyceride metabolism. The reliability of the metabolites were evaluated by the administration of Lilies, Rhizoma Anemarrhenas, Fluoxetine, and the combination of Lilies and Rhizoma Anemarrhenas, to the CUMS rats. Behavior studies demonstrated that treatment with the combination of Lilies and Rhizoma Anemarrhenas resulted in optimal antidepressant effects. The combination treatment was almost as effective as Fluoxetine. Our results suggest that Lilies and Rhizoma Anemarrhenae demonstrate synergistically antidepressant effects in CUMS via the regulation of multiple metabolic pathways. These findings provide insight into the pathophysiological mechanisms underlying CUMS and suggest innovative and effective treatments for this disorder. PMID: 28009452 [PubMed - as supplied by publisher]

Related Articles Integrative Analysis of PRKAG2 Cardiomyopathy iPS and Microtissue Models Identifies AMPK as a Regulator of Metabolism, Survival, and Fibrosis. Cell Rep. 2016 Dec 20;17(12):3292-3304 Authors: Hinson JT, Chopra A, Lowe A, Sheng CC, Gupta RM, Kuppusamy R, O'Sullivan J, Rowe G, Wakimoto H, Gorham J, Zhang K, Musunuru K, Gerszten RE, Wu SM, Chen CS, Seidman JG, Seidman CE Abstract AMP-activated protein kinase (AMPK) is a metabolic enzyme that can be activated by nutrient stress or genetic mutations. Missense mutations in the regulatory subunit, PRKAG2, activate AMPK and cause left ventricular hypertrophy, glycogen accumulation, and ventricular pre-excitation. Using human iPS cell models combined with three-dimensional cardiac microtissues, we show that activating PRKAG2 mutations increase microtissue twitch force by enhancing myocyte survival. Integrating RNA sequencing with metabolomics, PRKAG2 mutations that activate AMPK remodeled global metabolism by regulating RNA transcripts to favor glycogen storage and oxidative metabolism instead of glycolysis. As in patients with PRKAG2 cardiomyopathy, iPS cell and mouse models are protected from cardiac fibrosis, and we define a crosstalk between AMPK and post-transcriptional regulation of TGFβ isoform signaling that has implications in fibrotic forms of cardiomyopathy. Our results establish critical connections among metabolic sensing, myocyte survival, and TGFβ signaling. PMID: 28009297 [PubMed - in process]

Related Articles Metabonomic analysis of ovarian tumour cyst fluid by proton nuclear magnetic resonance spectroscopy. Oncotarget. 2016 Feb 09;7(6):7216-26 Authors: Kyriakides M, Rama N, Sidhu J, Gabra H, Keun HC, El-Bahrawy M Abstract The majority of ovarian tumours are of the epithelial type, which can be sub classified as benign, borderline or malignant. Epithelial tumours usually have cystic spaces filled with cyst fluid, the metabolic profile of which reflects the metabolic activity of the tumour cells, due to their close proximity. The approach of metabonomics using 1H-NMR spectroscopy was employed to characterize the metabolic profiles of ovarian cyst fluid samples (n = 23) from benign, borderline and malignant ovarian tumours in order to shed more light into ovarian tumour and cancer development. The analysis revealed that citrate was elevated in benign versus malignant tumours, while the amino acid lysine was elevated in malignant versus non-malignant tumours, both at a 5% significance level. Choline and lactate also had progressively increasing levels from benign to borderline to malignant samples. Finally, hypoxanthine was detected exclusively in a sub-cohort of the malignant tumours. This metabonomic study demonstrates that ovarian cyst fluid samples have potential to be used to distinguish between the different types of ovarian epithelial tumours. Furthermore, the respective metabolic profiles contain mechanistic information which could help identify biomarkers and therapeutic targets for ovarian tumours. PMID: 26769844 [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 2016/12/23PubMed comprises more than 24 million 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.

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 2016/12/22PubMed comprises more than 24 million 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.

Related Articles IL-6 Linkage to Exercise-Induced Shifts in Lipid-Related Metabolites: A Metabolomics-Based Analysis. J Proteome Res. 2016 Dec 20; Authors: Nieman DC, Sha W, Pappan KL Abstract Metabolomics profiling and bioinformatics technologies were used to determine the relationship between exercise-induced increases in IL-6 and lipid-related metabolites. Twenty-four male runners (age 36.5±1.8 y) ran on treadmills to exhaustion (2.26±0.01 h, 24.9±1.3 km, 69.7±1.9% VO2max). Vastus lateralis muscle biopsy and blood samples were collected before and immediately after running, and showed a 33.7±4.2% decrease in muscle glycogen, 39.0±8.8-, 2.4±0.3-, and 1.4±0.1-fold increases in plasma IL-6, IL-8, and MCP-1, respectively, and 95.0±18.9% and 158±20.6% increases in cortisol and epinephrine, respectively (all, P<0.001). The metabolomics analysis revealed changes in 209 metabolites, especially long- and medium-chain fatty acids, lipid peroxidation products, acylcarnitines, and ketone bodies. OPLS-DA modeling supported a strong separation in pre- and post-exercise samples (R2Y=0.964, Q2Y=0.902). OPLSR analysis failed to produce a viable model for the relationship between IL-6 and all lipid-related metabolites (R2Y = 0.76, Q2Y = - 0.0748). Multiple structure equation models were evaluated based on IL-6, with the best fit pathway model showing a linkage of exercise time to IL-6, then carnitine, and 13-methylmyristic acid (a marker for adipose tissue lipolysis) and sebacate. These metabolomics-based data indicate that the increase in plasma IL-6 after long endurance running has a minor relationship to increases in lipid-related metabolites. PMID: 27996272 [PubMed - as supplied by publisher]

Related Articles Metabolite Variation in Lean and Obese Streptozotocin (STZ)-Induced Diabetic Rats via (1)H NMR-Based Metabolomics Approach. Appl Biochem Biotechnol. 2016 Dec 19; Authors: Abu Bakar Sajak A, Mediani A, Maulidiani, Ismail A, Abas F Abstract Diabetes mellitus (DM) is considered as a complex metabolic disease because it affects the metabolism of glucose and other metabolites. Although many diabetes studies have been conducted in animal models throughout the years, the pathogenesis of this disease, especially between lean diabetes (ND + STZ) and obese diabetes (OB + STZ), is still not fully understood. In this study, the urine from ND + STZ, OB + STZ, lean/control (ND), and OB + STZ rats were collected and compared by using (1)H NMR metabolomics. The results from multivariate data analysis (MVDA) showed that the diabetic groups (ND + STZ and OB + STZ) have similarities and dissimilarities for a certain level of metabolites. Differences between ND + STZ and OB + STZ were particularly noticeable in the synthesis of ketone bodies, branched-chain amino acid (BCAA), and sensitivity towards the oral T2DM diabetes drug metformin. This finding suggests that the ND + STZ group was more similar to the T1DM model and OB + STZ to the T2DM model. In addition, we also managed to identify several pathways and metabolism aspects shared by obese (OB) and OB + STZ. The results from this study are useful in developing drug target-based research as they can increase understanding regarding the cause and effect of DM. PMID: 27995574 [PubMed - as supplied by publisher]

Related Articles Dataset of urinary metabolites measured by (1)H NMR analysis of normal human urine. Data Brief. 2017 Feb;10:227-229 Authors: Cassiède M, Nair S, Dueck M, Mino J, McKay R, Mercier P, Quémerais B, Lacy P Abstract The data in this article are related to the research entitled, "Assessment of (1)H NMR-based metabolomics analysis for normalization of urinary metals against creatinine" (M. Cassiède, S. Nair, M. Dueck, J. Mino, R. McKay, P. Mercier, B. Quémerais, P. Lacy, 2016) [1]. This article describes the analysis of urinary metabolites in normal, healthy individuals by (1)H NMR-based metabolomics. NMR spectra of urine samples typically contain hundreds of peaks that must be carefully screened for reproducibility and detectability. An important requirement in the screening of appropriate urinary metabolites is to ensure that they are reproducibly detected. In our study, we applied the peak profiles of 151 known urinary metabolites to 10 normal human urine samples and found that 50 metabolites were reproducibly measured between 600 and 700 MHz magnets in the same samples. The data set has been made publicly available to enable critical or extended analysis. PMID: 27995159 [PubMed]

Related Articles Harvest year effects on Apulian EVOOs evaluated by (1)H NMR based metabolomics. PeerJ. 2016;4:e2740 Authors: Girelli CR, Del Coco L, Papadia P, De Pascali SA, Fanizzi FP Abstract Nine hundred extra virgin olive oils (EVOO) were extracted from individual olive trees of four olive cultivars (Coratina, Cima di Mola, Ogliarola, Peranzana), originating from the provinces of Bari and Foggia (Apulia region, Southern Italy) and collected during two consecutive harvesting seasons (2013/14 and 2014/15). Following genetic identification of individual olive trees, a detailed Apulian EVOO NMR database was built using 900 oils samples obtained from 900 cultivar certified single trees. A study on the olive oil lipid profile was carried out by statistical multivariate analysis (Principal Component Analysis, PCA, Partial Least-Squares Discriminant Analysis, PLS-DA, Orthogonal Partial Least-Squares Discriminant Analysis, OPLS-DA). Influence of cultivar and weather conditions, such as the summer rainfall, on the oil metabolic profile have been evaluated. Mahalanobis distances and J2 criterion have been measured to assess the quality of resulting scores clusters for each cultivar in the two harvesting campaigns. The four studied cultivars showed non homogeneous behavior. Notwithstanding the geographical spread and the wide number of samples, Coratina showed a consistent behavior of its metabolic profile in the two considered harvests. Among the other three Peranzana showed the second more consistent behavior, while Cima di Mola and Ogliarola having the biggest change over the two years. PMID: 27994965 [PubMed]

Related Articles A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction. J Chromatogr A. 2016 Jun 24;1452:1-9 Authors: Fu HY, Guo JW, Yu YJ, Li HD, Cui HP, Liu PP, Wang B, Wang S, Lu P Abstract Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method. PMID: 27207578 [PubMed - indexed for MEDLINE]

Zinc stress affects ionome and metabolome in tea plants. Plant Physiol Biochem. 2016 Dec 12;111:318-328 Authors: Zhang Y, Wang Y, Ding Z, Wang H, Song L, Jia S, Ma D Abstract The research of physiological responses to Zn stress in plants has been extensively studied. However, the ionomics and metabolomics responses of plants to Zn stress remain largely unknown. In present study, the nutrient elements were identified involved in ion homeostasis and metabolomics changes related to Zn deficiency or excess in tea plants. Nutrient element analysis demonstrated that the concentrations of Zn affected the ion-uptake in roots and the nutrient element transportation to leaves, leading to the different distribution of P, S, Al, Ca, Fe and Cu in the tea leaves or roots. Metabolomics analysis revealed that Zn deficiency or excess differentially influenced the metabolic pathways in the tea leaves. More specifically, Zn deficiency affected the metabolism of carbohydrates, and Zn excess affected flavonoids metabolism. Additionally, the results showed that both Zn deficiency and Zn excess led to reduced nicotinamide levels, which speeded up NAD(+) degradation and thus reduced energy metabolism. Furthermore, element-metabolite correlation analysis illustrated that Zn contents in the tea leaves were positively correlated with organic acids, nitrogenous metabolites and some carbohydrate metabolites, and negatively correlated with the metabolites involved in secondary metabolism and some other carbohydrate metabolites. Meanwhile, metabolite-metabolite correlation analysis demonstrated that organic acids, sugars, amino acids and flavonoids played dominant roles in the regulation of the tea leaf metabolism under Zn stress. Therefore, the conclusion should be drawn that the tea plants responded to Zn stress by coordinating ion-uptake and regulation of metabolism of carbohydrates, nitrogenous metabolites, and flavonoids. PMID: 27992770 [PubMed - as supplied by publisher]

Comprehensive metabolomic and lipidomic profiling of human kidney tissue: a platform comparison. J Proteome Res. 2016 Dec 19; Authors: Leuthold P, Schaeffeler E, Winter S, Büttner F, Hofmann U, Mürdter TE, Rausch S, Sonntag D, Wahrheit J, Fend F, Hennenlotter J, Bedke J, Schwab M, Haag M Abstract Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for non-targeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the non-targeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms the majority (>90%) revealed Spearman's correlation coefficients ≥ 0.3, indicating that quantitative results from the non-targeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof-of-concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and non-tumorous material could be demonstrated based on unsupervised statistical analysis. PMID: 27992229 [PubMed - as supplied by publisher]

A non-targeted UHPLC-HRMS metabolomics pipeline for metabolite identification; application to cardiac remote ischemic preconditioning. Anal Chem. 2016 Dec 19; Authors: Kouassi Nzoughet J, Bocca C, Simard G, Prunier-Mirebeau D, Chao de la Barca JM, Bonneau D, Procaccio V, Prunier F, Lenaers G, Reynier P Abstract In recent years, the amount of investigations based on non-targeted metabolomics has increased, although often without thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated non-targeted metabolomics strategy with pipeline for unequivocal metabolites identification using the MSMLS™ molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS and MS/MS spectra. A UHPLC-HRMS Q-Exactive™ method was developed and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from Remote Ischemic Pre-Conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites VIP values and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the Metabolomics Standard Initiative and confirmed by both RPLC and HILIC-HRMS. PMID: 27992159 [PubMed - as supplied by publisher]

Comfortably numb and back: Plasma metabolomics reveals biochemical adaptations in the hibernating thirteen-lined ground squirrel. J Proteome Res. 2016 Dec 19; Authors: D'Alessandro A, Nemkov T, Bogren LK, Martin SL, Hansen KC Abstract Hibernation is an evolutionary adaptation affording some mammals the ability to exploit the cold to achieve extreme metabolic depression (torpor) whilst avoiding ischemia/reperfusion or hemorrhagic shock injuries. Hibernators cycle periodically out of torpor, restoring high metabolic activity. If understood at the molecular level, the adaptations underlying torpor-arousal cycles may be leveraged for translational applications in critical fields such as intensive care medicine. Here, we monitored 266 metabolites to investigate the metabolic adaptations to hibernation in plasma from thirteen-lined ground squirrels (57 animals, 9 timepoints). Results indicate that the periodic arousals foster the removal of potentially toxic oxidative stress-related metabolites which accumulate in plasma during torpor while replenishing reservoirs of circulating catabolic substrates (free fatty acids and amino acids). Specifically, we identified metabolic fluctuations of basic amino acids lysine and arginine, one-carbon metabolism intermediates and sulfur-containing metabolites methionine, cysteine and cystathionine. Conversely, reperfusion injury markers such as succinate/fumarate remained relatively stable across cycles. Considering the cycles of these metabolites with the hibernator's cycling metabolic activity together with their well-established role as substrates for the production of hydrogen sulfide (H2S), we hypothesize that these metabolic fluctuations function as a biological clock regulating torpor to arousal transitions and resistance to reperfusion during arousal. PMID: 27991798 [PubMed - as supplied by publisher]