PubMed

Related Articles METABOLOMIC-DRIVEN ELUCIDATION OF SERUM DISTURBANCES ASSOCIATED WITH ALZHEIMER´S DISEASE AND MILD COGNITIVE IMPAIRMENT. Curr Alzheimer Res. 2016 Jan 28; Authors: González-Domínguez R, Rupérez FJ, García-Barrera T, Barbas C, Gómez-Ariza JL Abstract Numerous efforts have been made in the last years to discover potential biomarkers of Alzheimer's disease and its progression from mild cognitive impairment, considered as an intermediate phase in the development of Alzheimer's disease from normal aging. However, there is still a considerable lack of understanding about pathological mechanisms underlying to disease. In the present study, serum metabolomics based on ultra-high-performance liquid chromatography-mass spectrometry was applied to investigate metabolic differences between subjects with Alzheimer's disease and mild cognitive impairment, as well as healthy controls. The most important findings can be associated with impaired metabolism of phospholipids and sphingolipids leading to membrane breakdown, wherein the nature of the fatty acids contained in the structure in terms of acyl chain length and degree of unsaturation appears to play a crucial role. Furthermore, several discriminant metabolites were found for the first time in relation to known pathological processes associated with Alzheimer's disease, such as the accumulation of acylcarnitines in relation to mitochondrial dysfunction, decreased levels of oleamide and monoglycerides as a result of defects in endocannabinoid system, or increased serum phenylacetylglutamine, which could reveal alterations in glutamine homeostasis. Therefore, these results represent a suitable approximation to understand the pathogenesis and progression of the disease.  . PMID: 26825096 [PubMed - as supplied by publisher]

Related Articles Urinary Metabolomics Reveals Alterations of Aromatic Amino Acid Metabolism of Alzheimer's Disease in the Transgenic CRND8 Mice. Curr Alzheimer Res. 2016 Jan 28; Authors: Tang Z, Liu L, Li Y, Dong J, Li M, Huang J, Lin S, Cai Z Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with amyloid plaques accumulation as the key feature involved in its pathology. To date, however, the biochemical changes in AD have not been clearly characterized. Here, we present that urinary metabolomics based on high resolution mass spectrometry was employed for delineation of metabolic alterations in transgenic CRND8 mice. In this noninvasive approach, urinary metabolome reveals the biochemical changes in early onset of this AD mouse model. In virtue of comprehensive metabolite profiling and multivariate statistical analysis, a total of 73 differential metabolites of urine sample sets was identified in 12-week and 18-week transgenic mice compared to wild-type littermates, covering perturbations of aromatic amino acids metabolism, the Krebs cycle and one-carbon metabolism. Of particular interest is that divergent tryptophan metabolism, such as upregulation of serotonin pathway while downregulation of kynurenine pathway, was observed. Meanwhile, the accumulation of both N-acetylvanilalanine and 3-methoxytyrosine indicated aromatic L-amino acid decarboxylase deficiency. And the microbial metabolites derived from aromatic amino acid metabolism and drug-like phase II metabolic response via the glycine conjugation reactions were also highlighted, indicating that genetic modification in mouse brain not only alters genotype but also perturbs the gut microbiome. Together, our study demonstrated that the integrative approach employing mass spectrometry-based metabolomics and a transgenic mouse model for AD may provide new evidence for distinct metabolic signatures. The perturbations of metabolic pathways may have far-reaching implications for early diagnosis and intervention in AD. PMID: 26825095 [PubMed - as supplied by publisher]

Metabolic Effects of the pksCT Gene on Monascus aurantiacus Li As3.4384 Using Gas Chromatography-Time-of-Flight Mass Spectrometry-Based Metabolomics. J Agric Food Chem. 2016 Jan 29; Authors: Huang Z, Zhang S, Xu Y, Li L, Li Y Abstract Monascus spp. have been used for the production of natural pigments and bioactive compounds in China for several centuries. Monascus also can produce the mycotoxin citrinin, restricting its use. Disruption of the pksCT gene in Monascus aurantiacus Li AS3.4384 reduces citrinin production capacity of this strain (Monascus PHDS26) by over 98%. However, it is unclear how other metabolites of M. aurantiacus Li AS3.4384 (the wild-type strain) are affected by the pksCT gene. Here, we used metabolomics analyses to compare red yeast rice (RYR) metabolite profiles of the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. Eighteen metabolites forming components within the glycolysis, acetyl-CoA, amino acid, and TCA cycle metabolic processes were found to be altered between the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. Thus, these findings provide important insights into the metabolic pathways affected by the pksCT gene in M. aurantiacus. PMID: 26824776 [PubMed - as supplied by publisher]

Reply to letter 16-005. Liver Transpl. 2016 Jan 29; Authors: Lahoz A PMID: 26824515 [PubMed - as supplied by publisher]

Statistical correlations between NMR spectroscopy and direct infusion FT-ICR mass spectrometry aid annotation of unknowns in metabolomics. Anal Chem. 2016 Jan 29; Authors: Hao J, Liebeke M, Sommer U, Viant MR, Bundy JG, Ebbels TM Abstract NMR spectroscopy and mass spectrometry are the two major analytical platforms for metabolomics, and both generate substantial data with hundreds to thousands of observed peaks for a single sample. Many of these are unknown, and peak assignment is generally complex and time-consuming. Statistical correlations between data types have proven useful in expediting this process, for example in prioritizing candidate assignments. However, this approach has not been formally assessed for the comparison of direct-infusion mass spectrometry (DIMS) and NMR data. Here, we present a systematic analysis of a sample set (tissue extracts), and the utility of a simple correlation thresh-old to aid metabolite identification. The correlations were surprisingly successful in linking structurally related signals, with 15 of 26 NMR-detectable metabolites having their highest correlation to a cognate MS ion. However, we found that the distribution of the correlations was highly dependent on the nature of the MS ion, such as the adduct type. This approach should help to alleviate this important bottleneck where both 1D NMR and DIMS datasets have been collected. PMID: 26824414 [PubMed - as supplied by publisher]

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway. Sci Adv. 2016 Jan;2(1):e1501235 Authors: Keller MA, Zylstra A, Castro C, Turchyn AV, Griffin JL, Ralser M Abstract Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks. PMID: 26824074 [PubMed]

The Need for Biomarkers in Diagnosis and Prognosis of Drug-Induced Liver Disease: Does Metabolomics Have Any Role? Biomed Res Int. 2015;2015:386186 Authors: Iruzubieta P, Arias-Loste MT, Barbier-Torres L, Martinez-Chantar ML, Crespo J Abstract Drug-induced liver injury (DILI) is a potentially fatal adverse event and the leading cause of acute liver failure in the US and in the majority of Europe. The liver can be affected directly, in a dose-dependent manner, or idiosyncratically, independently of the dose, and therefore unpredictably. Currently, DILI is a diagnosis of exclusion that physicians should suspect in patients with unexplained elevated liver enzymes. Therefore, new diagnostic and prognostic biomarkers are necessary to achieve an early and reliable diagnosis of DILI and thus improve the prognosis. Although several DILI biomarkers have been found through analytical and genetic tests and pharmacokinetic approaches, none of them have been able to display enough specificity and sensitivity, so new approaches are needed. In this sense, metabolomics is a strongly and promising emerging field that, from biofluids collected through minimally invasive procedures, can obtain early biomarkers of toxicity, which may constitute specific indicators of liver damage. PMID: 26824035 [PubMed - in process]

The metabolomic profile of gamma-irradiated human hepatoma and muscle cells reveals metabolic changes consistent with the Warburg effect. PeerJ. 2016;4:e1624 Authors: Wang M, Keogh A, Treves S, Idle JR, Beyoğlu D Abstract The two human cell lines HepG2 from hepatoma and HMCL-7304 from striated muscle were γ-irradiated with doses between 0 and 4 Gy. Abundant γH2AX foci were observed at 4 Gy after 4 h of culture post-irradiation. Sham-irradiated cells showed no γH2AX foci and therefore no signs of radiation-induced double-strand DNA breaks. Flow cytometry indicated that 41.5% of HepG2 cells were in G2/M and this rose statistically significantly with increasing radiation dose reaching a plateau at ∼47%. Cell lysates from both cell lines were subjected to metabolomic analysis using Gas Chromatography-Mass Spectrometry (GCMS). A total of 46 metabolites could be identified by GCMS in HepG2 cell lysates and 29 in HMCL-7304 lysates, most of which occurred in HepG2 cells. Principal Components Analysis (PCA) showed a clear separation of sham, 1, 2 and 4 Gy doses. Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA) revealed elevations in intracellular lactate, alanine, glucose, glucose 6-phosphate, fructose and 5-oxoproline, which were found by univariate statistics to be highly statistically significantly elevated at both 2 and 4 Gy compared with sham irradiated cells. These findings suggested upregulation of cytosolic aerobic glycolysis (the Warburg effect), with potential shunting of glucose through aldose reductase in the polyol pathway, and consumption of reduced Glutathione (GSH) due to γ-irradiation. In HMCL-7304 myotubes, a putative Warburg effect was also observed only at 2 Gy, albeit a lesser magnitude than in HepG2 cells. It is anticipated that these novel metabolic perturbations following γ-irradiation of cultured cells will lead to a fuller understanding of the mechanisms of tissue damage following ionizing radiation exposure. PMID: 26823999 [PubMed]

Modeling therapy response and spatial tissue distribution of erlotinib in pancreatic cancer. Mol Cancer Ther. 2016 Jan 28; Authors: Grüner BM, Winkelmann I, Feuchtinger A, Sun N, Balluff B, Teichmann N, Herner A, Kalideris E, Steiger K, Braren R, Aichler M, Esposito I, Schmid RM, Walch A, Siveke JT Abstract Pancreatic ductal adenocarcinoma (PDAC) is likely the most aggressive and therapy-resistant of all cancers. Aim of this study was to investigate the emerging technology of matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) as a powerful tool to study drug delivery and spatial tissue distribution in PDAC. We utilized an established genetically engineered mouse model of spontaneous PDAC to examine the distribution of the small molecule inhibitor erlotinib in healthy pancreas and PDAC. MALDI IMS was utilized on sections of single-dose or long-term-treated mice to measure drug tissue distribution. Histological and statistical analyses were performed to correlate morphology, drug distribution and survival. We found that erlotinib levels were significantly lower in PDAC compared to healthy tissue (p = 0.0078). Survival of long-term-treated mice did not correlate with overall levels of erlotinib or with overall histological tumor grade but both with the percentage of atypical glands in the cancer (p = 0.021, rs = 0.59) and the level of erlotinib in those atypical glands (p = 0.019, rs = 0.60). The results of this pilot study present MALDI IMS as a reliable technology to study drug delivery and spatial distribution compounds in a preclinical setting and supports drug imaging-based translational approaches. PMID: 26823494 [PubMed - as supplied by publisher]

ShockOmics: multiscale approach to the identification of molecular biomarkers in acute heart failure induced by shock. Scand J Trauma Resusc Emerg Med. 2016;24(1):9 Authors: Aletti F, Conti C, Ferrario M, Ribas V, Bollen Pinto B, Herpain A, Post E, Romay Medina E, Barlassina C, de Oliveira E, Pastorelli R, Tedeschi G, Ristagno G, Taccone FS, Schmid-Schönbein GW, Ferrer R, De Backer D, Bendjelid K, Baselli G Abstract BACKGROUND: The ShockOmics study (ClinicalTrials.gov identifier NCT02141607) is a multicenter prospective observational trial aimed at identifying new biomarkers of acute heart failure in circulatory shock, by means of a multiscale analysis of blood samples and hemodynamic data from subjects with circulatory shock. METHODS AND DESIGN: Ninety septic shock and cardiogenic shock patients will be recruited in three intensive care units (ICU) (Hôpital Erasme, Université Libre de Bruxelles, Belgium; Hospital Universitari Mutua Terrassa, Spain; Hôpitaux Universitaires de Genève, Switzerland). Hemodynamic signals will be recorded every day for up to seven days from shock diagnosis (time T0). Clinical data and blood samples will be collected for analysis at: i) T1 < 16 h from T0; ii) T2 = 48 h after T0; iii) T3 = day 7 or before discharge or before discontinuation of therapy in case of fatal outcome; iv) T4 = day 100. The inclusion criteria are: shock, Sequential Organ Failure Assessment (SOFA) score > 5 and lactate levels ≥ 2 mmol/L. The exclusion criteria are: expected death within 24 h since ICU admission; > 4 units of red blood cells or >1 fresh frozen plasma transfused; active hematological malignancy; metastatic cancer; chronic immunodepression; pre-existing end stage renal disease requiring renal replacement therapy; recent cardiac surgery; Child-Pugh C cirrhosis; terminal illness. Enrollment will be preceded by the signature of the Informed Consent by the patient or his/her relatives and by the physician in charge. Three non-shock control groups will be included in the study: a) healthy blood donors (n = 5); b) septic patients (n = 10); c) acute myocardial infarction or patients with prolonged acute arrhythmia (n = 10). The hemodynamic data will be downloaded from the ICU monitors by means of dedicated software. The blood samples will be utilized for transcriptomics, proteomics and metabolomics ("-omics") analyses. DISCUSSION: ShockOmics will provide new insights into the pathophysiological mechanisms underlying shock as well as new biomarkers for the timely diagnosis of cardiac dysfunction in shock and quantitative indices for assisting the therapeutic management of shock patients. PMID: 26822963 [PubMed - in process]

Evaluation of O2PLS in Omics data integration. BMC Bioinformatics. 2016;17 Suppl 2:11 Authors: Bouhaddani SE, Houwing-Duistermaat J, Salo P, Perola M, Jongbloed G, Uh HW Abstract BACKGROUND: Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation. RESULTS: A simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret. CONCLUSIONS: Simulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation. PMID: 26822911 [PubMed - in process]

Metabolomics in amyotrophic lateral sclerosis: how far can it take us? Eur J Neurol. 2016 Jan 29; Authors: Blasco H, Patin F, Madji Hounoum B, Gordon PH, Vourc'h P, Andres CR, Corcia P Abstract Amyotrophic lateral sclerosis (ALS) is the most common adult-onset motor neuron disease. Alongside identification of aetiologies, development of biomarkers is a foremost research priority. Metabolomics is one promising approach that is being utilized in the search for diagnosis and prognosis markers. Our aim is to provide an overview of the principal research in metabolomics applied to ALS. References were identified using PubMed with the terms 'metabolomics' or 'metabolomic' and 'ALS' or 'amyotrophic lateral sclerosis' or 'MND' or 'motor neuron disorders'. To date, nine articles have reported metabolomics research in patients and a few additional studies examined disease physiology and drug effects in patients or models. Metabolomics contribute to a better understanding of ALS pathophysiology but, to date, no biomarker has been validated for diagnosis, principally due to the heterogeneity of the disease and the absence of applied standardized methodology for biomarker discovery. A consensus on best metabolomics methodology as well as systematic independent validation will be an important accomplishment on the path to identifying the long-awaited biomarkers for ALS and to improve clinical trial designs. PMID: 26822316 [PubMed - as supplied by publisher]

Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat Commun. 2016;7:10558 Authors: Hartiala JA, Tang WH, Wang Z, Crow AL, Stewart AF, Roberts R, McPherson R, Erdmann J, Willenborg C, Hazen SL, Allayee H Abstract Metabolites derived from dietary choline and L-carnitine, such as trimethylamine N-oxide and betaine, have recently been identified as novel risk factors for atherosclerosis in mice and humans. We sought to identify genetic factors associated with plasma betaine levels and determine their effect on risk of coronary artery disease (CAD). A two-stage genome-wide association study (GWAS) identified two significantly associated loci on chromosomes 2q34 and 5q14.1. The lead variant on 2q24 (rs715) localizes to carbamoyl-phosphate synthase 1 (CPS1), which encodes a mitochondrial enzyme that catalyses the first committed reaction and rate-limiting step in the urea cycle. Rs715 is also significantly associated with decreased levels of urea cycle metabolites and increased plasma glycine levels. Notably, rs715 yield a strikingly significant and protective association with decreased risk of CAD in only women. These results suggest that glycine metabolism and/or the urea cycle represent potentially novel sex-specific mechanisms for the development of atherosclerosis. PMID: 26822151 [PubMed - in process]

Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief Bioinform. 2016 Jan 27; Authors: Frainay C, Jourdan F Abstract Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods. PMID: 26822099 [PubMed - as supplied by publisher]

Related Articles The CDP-Ethanolamine Pathway Regulates Skeletal Muscle Diacylglycerol Content and Mitochondrial Biogenesis without Altering Insulin Sensitivity. Cell Metab. 2015 May 5;21(5):718-30 Authors: Selathurai A, Kowalski GM, Burch ML, Sepulveda P, Risis S, Lee-Young RS, Lamon S, Meikle PJ, Genders AJ, McGee SL, Watt MJ, Russell AP, Frank M, Jackowski S, Febbraio MA, Bruce CR Abstract Accumulation of diacylglycerol (DG) in muscle is thought to cause insulin resistance. DG is a precursor for phospholipids, thus phospholipid synthesis could be involved in regulating muscle DG. Little is known about the interaction between phospholipid and DG in muscle; therefore, we examined whether disrupting muscle phospholipid synthesis, specifically phosphatidylethanolamine (PtdEtn), would influence muscle DG content and insulin sensitivity. Muscle PtdEtn synthesis was disrupted by deleting CTP:phosphoethanolamine cytidylyltransferase (ECT), the rate-limiting enzyme in the CDP-ethanolamine pathway, a major route for PtdEtn production. While PtdEtn was reduced in muscle-specific ECT knockout mice, intramyocellular and membrane-associated DG was markedly increased. Importantly, however, this was not associated with insulin resistance. Unexpectedly, mitochondrial biogenesis and muscle oxidative capacity were increased in muscle-specific ECT knockout mice and were accompanied by enhanced exercise performance. These findings highlight the importance of the CDP-ethanolamine pathway in regulating muscle DG content and challenge the DG-induced insulin resistance hypothesis. PMID: 25955207 [PubMed - indexed for MEDLINE]

Related Articles Comparative metabolic fingerprinting of Gentiana rhodantha from different geographical origins using LC-UV-MS/MS and multivariate statistical analysis. BMC Biochem. 2015;16:9 Authors: Pan Y, Zhang J, Shen T, Zhao YL, Wang YZ, Li WY Abstract BACKGROUNDS: Gentiana rhodantha, a rich source of iridoids and polyphenols, is a traditional ethnomedicine widely used in China. Metabolic fingerprinting based on a LC-UV-MS/MS method was applied to explore the chemical markers for discrimination of G. rhodantha from different geographical origins. RESULTS: Targeted compounds were separated on a Shim-pack XR-ODS III (150 × 2.0 mm, 2.2 μm), with a mobile phase consisted of acetonitrile and 0.1% formic acid in water, under gradient elution. In quantitative analysis, all of the calibration curves showed good linear regression (R(2) < less than 0.9991) within the tested ranges, and accuracy ranged from 97.8% to 104.2% and the %RSD of precision (less than 3%) were all within the required limits. The most abundant mangiferin (82.21 mg/g) found in sample from Zunyi, Guizhou province. Furthermore, 64 samples according to their geographical origins, could be classified by partial least-squares discriminate analysis (PLS-DA) and nine compounds including two new compounds identified by mass spectrometry could be regarded as characteristic compounds for discriminating samples from different geographical origins. CONCLUSIONS: The developed method appears to be a useful tool for analysis of G. rhodantha, which could provide potential indicators for differentiation of different geographical origins. PMID: 25880482 [PubMed - indexed for MEDLINE]

Isotopic Ratio Outlier Analysis (IROA) of the S. cerevisiae metabolome using accurate mass GC-TOF/MS: A new method for discovery. Anal Chem. 2016 Jan 28; Authors: Qiu Y, Moir R, Willis IM, Beecher C, Tsai YH, Garrett TJ, Yost RA, Kurland IJ Abstract Isotopic Ratio Outlier Analysis (IROA) is a 13C metabolomics profiling method that eliminates sample-to-sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass LC/MS. This is the first report using IROA technology in combination with accurate mass GC-TOFMS, here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% 13C, or 5%13C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%13C extracts, or light isotopologues in the 95%13C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the 12C monoisotopic and the 13C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both Chemical and Electron Ionization, extends the information acquired from the isotopic peak patterns for formulae generation, a process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations, are used as search constraints. In Electron Impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of Chemical Ionization (CI) IROA and EI IROA affords a metabolite identification procedure that enables the identification of co-eluting metabolites, and allowed us to characterize 126 metabolites in the current study. PMID: 26820234 [PubMed - as supplied by publisher]

Method for the Compound Annotation of Conjugates in Nontargeted Metabolomics Using Accurate Mass Spectrometry, Multistage Product Ion Spectra and Compound Database Searching. Mass Spectrom (Tokyo). 2015;4(1):A0036 Authors: Ogura T, Bamba T, Tai A, Fukusaki E Abstract Owing to biotransformation, xenobiotics are often found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a sufficient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider. The annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When finished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. The results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation. PMID: 26819907 [PubMed]

Multi-Component Profiling of Trace Volatiles in Blood by Gas Chromatography/Mass Spectrometry with Dynamic Headspace Extraction. Mass Spectrom (Tokyo). 2015;4(1):A0034 Authors: Kakuta S, Yamashita T, Nishiumi S, Yoshida M, Fukusaki E, Bamba T Abstract A dynamic headspace extraction method (DHS) with high-pressure injection is described. This dynamic extraction method has superior sensitivity to solid phase micro extraction, SPME and is capable of extracting the entire gas phase by purging the headspace of a vial. Optimization of the DHS parameters resulted in a highly sensitive volatile profiling system with the ability to detect various volatile components including alcohols at nanogram levels. The average LOD for a standard volatile mixture was 0.50 ng mL(-1), and the average LOD for alcohols was 0.66 ng mL(-1). This method was used for the analysis of volatile components from biological samples and compared with acute and chronic inflammation models. The method permitted the identification of volatiles with the same profile pattern as in vitro oxidized lipid-derived volatiles. In addition, the concentration of alcohols and aldehydes from the acute inflammation model samples were significantly higher than that for the chronic inflammation model samples. The different profiles between these samples could also be identified by this method. Finally, it was possible to analyze alcohols and low-molecular-weight volatiles that are difficult to analyze by SPME in high sensitivity and to show volatile profiling based on multi-volatile simultaneous analysis. PMID: 26819905 [PubMed]

Ketones Step to the Plate: A Game Changer for Metabolic Remodeling in Heart Failure? Circulation. 2016 Jan 27; Authors: Kolwicz SC, Airhart S, Tian R Abstract It is increasingly recognized that metabolic remodeling is integral to heart failure development and progression.(1,2) In particular, impairments in the ability of cardiac mitochondria to oxidize fatty acids have been noted along with an increase in glycolysis that is uncoupled from glucose oxidation.(3,4) This overall reduction in the myocardial oxidative capacity is purported to be the root cause of energy deficiency in the failing heart. Although past research has primarily focused on myocardial use of glucose and fatty acids, the heart is an omnivore and capable of oxidizing other substrates such as lactate, ketone bodies, and amino acids. The current understanding of the contribution of lactate, ketone bodies, and amino acids to cardiac metabolism is limited, particularly in the setting of heart failure. In this issue of Circulation, two independent studies shed new insights on the reliance of the failing heart on ketone bodies for energy supply. Proteomics analysis in mouse models of heart failure by Aubert et al(5) and metabolomics analysis of end-stage human failing hearts by Bedi et al(6) demonstrate strong and concordant evidence of increased ketone oxidation in the failing heart. PMID: 26819375 [PubMed - as supplied by publisher]