PubMed

Related Articles Untargeted metabolomics studies employing NMR and LC-MS reveal metabolic coupling between Nanoarcheum equitans and its archaeal host Ignicoccus hospitalis. Metabolomics. 2015 Aug 1;11(4):895-907 Authors: Hamerly T, Tripet BP, Tigges M, Giannone RJ, Wurch L, Hettich RL, Podar M, Copié V, Bothner B Abstract Interspecies interactions are the basis of microbial community formation and infectious diseases. Systems biology enables the construction of complex models describing such interactions, leading to a better understanding of disease states and communities. However, before interactions between complex organisms can be understood, metabolic and energetic implications of simpler real-world host-microbe systems must be worked out. To this effect, untargeted metabolomics experiments were conducted and integrated with proteomics data to characterize key molecular-level interactions between two hyperthermophilic microbial species, both of which have reduced genomes. Metabolic changes and transfer of metabolites between the archaea Ignicoccus hospitalis and Nanoarcheum equitans were investigated using integrated LC-MS and NMR metabolomics. The study of such a system is challenging, as no genetic tools are available, growth in the laboratory is challenging, and mechanisms by which they interact are unknown. Together with information about relative enzyme levels obtained from shotgun proteomics, the metabolomics data provided useful insights into metabolic pathways and cellular networks of I. hospitalis that are impacted by the presence of N. equitans, including arginine, isoleucine, and CTP biosynthesis. On the organismal level, the data indicate that N. equitans exploits metabolites generated by I. hospitalis to satisfy its own metabolic needs. This finding is based on N. equitans's consumption of a significant fraction of the metabolite pool in I. hospitalis that cannot solely be attributed to increased biomass production for N. equitans. Combining LC-MS and NMR metabolomics datasets improved coverage of the metabolome and enhanced the identification and quantitation of cellular metabolites. PMID: 26273237 [PubMed - as supplied by publisher]

Related Articles Natural variation of plant metabolism: genetic mechanisms, interpretive caveats, evolutionary and mechanistic insights. Plant Physiol. 2015 Aug 13; Authors: Soltis NE, Kliebenstein DJ Abstract Combining quantitative genetics studies with metabolomics/metabolic profiling platforms, genomics and transcriptomics is creating significant progress in identifying the causal genes controlling natural variation in metabolite accumulations and profiles. In this review, we will discuss key mechanistic and evolutionary insights that are arising from these studies. This includes the potential role of transport and other processes in leading to a separation of the site of mechanistic causation and metabolic consequence. A re-illuminated observation is the potential for genomic variation in the organelle to alter phenotypic variation alone and in epistatic interaction with the nuclear genetic variation. These studies are also highlighting new aspects of metabolic pleiotropy both in terms of the breadth of loci altering metabolic variation as well as the potential for broader effects on plant defense regulation of the metabolic variation than has previously been predicted. We also illustrate caveats that can be overlooked when translating quantitative genetics descriptors such as heritability and per locus r2 to mechanistic or evolutionary interpretations. PMID: 26272883 [PubMed - as supplied by publisher]

Related Articles Targeted Metabolomics for Homocysteine-Related Metabolites in Primary Hepatocytes. Methods Mol Biol. 2015;1250:267-77 Authors: Selicharová I, Kořínek M Abstract Liquid chromatography-tandem mass spectrometry has become the most convenient method to identify and quantify low molecular weight metabolites from various sources. Metabolomics studies of hepatocytes hold promise for the identification of the mechanisms of toxicant-related disease processes. In this chapter, we present a rapid and sensitive liquid chromatography-tandem mass spectrometry method for the quantification of intracellular concentrations of nine homocysteine-based metabolites, namely homocysteine, methionine, cysteine, dimethylglycine, cystathionine, S-adenosylmethionine, S-adenosylhomocysteine, choline, and betaine. The method is specifically designed for the analysis of cultured primary hepatocytes. PMID: 26272149 [PubMed - in process]

Related Articles Transcriptomics of Hepatocytes Treated with Toxicants for Investigating Molecular Mechanisms Underlying Hepatotoxicity. Methods Mol Biol. 2015;1250:225-40 Authors: Shinde V, Stöber R, Nemade H, Sotiriadou I, Hescheler J, Hengstler J, Sachinidis A Abstract Transcriptomics is a powerful tool for high-throughput gene expression profiling. Transcriptome microarray experiments conducted with RNA isolated from hepatocytes after exposure to toxicants enable a deep insight into the molecular mechanisms of hepatotoxicity. This understanding, along with structure-activity relationships underlying hepatotoxicity, will provide a novel strategy to design cost-effective and safer therapeutics. Transcriptomics studies conducted with established hepatotoxic drugs in various in vitro and in vivo hepatotoxicity test systems have contributed to the elucidation of the mechanistic basis of liver insults, which were later on substantiated at the proteomics and metabolomics levels. The present chapter is focused on comprehensive transcriptomics of cultured primary hepatocytes treated with chemicals by applying Affymetrix microarray technology. It also describes the detailed protocol for culturing of hepatocytes, their exposure to toxicants as well as sample collection, including RNA isolation, RNA target preparation and finally the hybridization to gene chips for microarray expression analysis. PMID: 26272146 [PubMed - in process]

Related Articles Red blood cell storage in additive solution-7 preserves energy and redox metabolism: a metabolomics approach. Transfusion. 2015 Aug 14; Authors: D'Alessandro A, Nemkov T, Hansen KC, Szczepiorkowski ZM, Dumont LJ Abstract BACKGROUND: Storage and transfusion of red blood cells (RBCs) has a huge medical and economic impact. Routine storage practices can be ameliorated through the implementation of novel additive solutions (ASs) that tackle the accumulation of biochemical and morphologic lesion during routine cold liquid storage in the blood bank, such as the recently introduced alkaline solution AS-7. Here we hypothesize that AS-7 might exert its beneficial effects through metabolic modulation during routine storage. STUDY DESIGN AND METHODS: Apheresis RBCs were resuspended either in control AS-3 or experimental AS-7, before ultrahigh-performance liquid chromatography-mass spectrometry metabolomics analysis. RESULTS: Unambiguous assignment and relative quantitation was achieved for 229 metabolites. AS-3 and AS-7 results in many similar metabolic trends over storage, with AS-7 RBCs being more metabolically active in the first storage week. AS-7 units had faster fueling of the pentose phosphate pathway, higher total glutathione pools, and increased flux through glycolysis as indicated by higher levels of pathway intermediates. Metabolite differences are especially observed at 7 days of storage, but were still maintained throughout 42 days. CONCLUSION: AS-7 formulation (chloride free and bicarbonate loading) appears to improve energy and redox metabolism in stored RBCs in the early storage period, and the differences, though diminished, are still appreciable by Day 42. Energy metabolism and free fatty acids should be investigated as potentially important determinants for preservation of RBC structure and function. Future studies will be aimed at identifying metabolites that correlate with in vitro and in vivo circulation times. PMID: 26271632 [PubMed - as supplied by publisher]

Related Articles Type 2 diabetes is associated with postprandial amino acid measures. Arch Biochem Biophys. 2015 Aug 10; Authors: Mook-Kanamori DO, de Mutsert R, Rensen PC, Prehn C, Adamski J, Heijer MD, le Cessie S, Suhre K, Rosendaal FR, Willems van Dijk K Abstract Most studies examining the association between type 2 diabetes (T2D) and amino acids have focused on fasting concentrations. We hypothesized that, besides fasting concentrations, amino acid responses to a standardized meal challenge are also associated with T2D. In a cross-sectional study of 525 participants (165 newly-diagnosed T2D, 186 newly-diagnosed impaired fasting glycaemia, and 174 normal fasting glucose), we examined postprandial amino acid concentrations and the responses (defined as the concentrations and responses 150 minutes after a standardized meal) of fourteen amino acids in relation to T2D. T2D was associated with lower postprandial concentration of seven amino acids compared to the normal fasting glucose group (lowest effect estimate for serine: -0.54 standard deviations (SD) (95% CI: -0.77, -0.32)), and higher concentrations of phenylalanine, tryptophan, tyrosine and (iso-)leucine (highest effect estimate for (iso-)leucine: 0.44 SD (95% CI: 0.20, 0.67)). Regarding the meal responses, T2D was associated with lower responses of seven amino acids (ranging from -0.55 SD ((95% CI): -0.78, -0.33) for serine to -0.25 SD ((95% CI: 0.-0.45, -0.02) for ornithine). We conclude that T2D is associated with postprandial concentrations of amino acids and a reduced amino acid meal response, indicating that these measures may also be potential markers of T2D. PMID: 26271442 [PubMed - as supplied by publisher]

Related Articles Serum Lipid and Serum Metabolite Components in relation to anthropometric parameters in EPIC-Potsdam participants. Metabolism. 2015 Jul 11; Authors: Foerster J, Hyötyläinen T, Oresic M, Nygren H, Boeing H Abstract BACKGROUND/AIM: Lipidomic and metabolomic techniques become more and more important in human health research. Recent developments in analytical techniques enable the investigation of high amounts of substances. The high numbers of metabolites and lipids that are detected with among others mass spectrometric techniques challenge in most cases the statistical processes to bring out stable and interpretable results. This study targets to use the novel non-established statistical method treelet transform (TT) to investigate high numbers of metabolites and lipids and to compare the results with the established method principal component analysis (PCA). Serum lipid and metabolite profiles are investigated regarding their association to anthropometric parameters associated to obesity. METHODS: From 226 participants of the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam study blood samples were investigated with an untargeted metabolomics approach regarding serum metabolites and lipids. Additionally, participants were surveyed anthropometrically to assess parameters of obesity, such as body mass index (BMI), waist-to-hip-ratio (WHR) and body fat mass. TT and PCA are used to generate treelet components (TCs) and factors summarizing serum metabolites and lipids in new, latent variables without too much loss of information. With partial correlations TCs and factors were associated to anthropometry under the control for relevant parameters, such as sex and age. RESULTS: TT with metabolite variables (p=121) resulted in 5 stable and interpretable TCs explaining 18.9% of the variance within the data. PCA on the same variables generated 4 quite complex, less easily interpretable factors explaining 37.5% of the variance. TT on lipidomic data (p=353) produced 3 TCs as well as PCA on the same data resulted in 3 factors; the proportion of explained variance was 17.8% for TT and 39.8% for PCA. In both investigations TT ended up with stable components that are easier to interpret than the factors from the PCA. In general, the generated TCs and factors were similar in their structure when the factors are considered regarding the original variables loading high on them. Both TCs and factors showed associations to anthropometric measures. CONCLUSIONS: TT is a suitable statistical method to generate summarizing, latent variables in data sets with more variables than observations. In the present investigation it resulted in similar latent variables compared to the established method of PCA. Whereby less variance is explained by the summarizing constructs of TT compared to the factors of PCA, TCs are easier to interpret. Additionally the resulting TCs are quite stable in bootstrap samples. PMID: 26271139 [PubMed - as supplied by publisher]

Related Articles Serum metabonomic analysis of apoE(-/-) mice reveals progression axes for atherosclerosis based on NMR spectroscopy. Mol Biosyst. 2014 Dec;10(12):3170-8 Authors: Yang Y, Liu Y, Zheng L, Wu T, Li J, Zhang Q, Li X, Yuan F, Wang L, Guo J Abstract Atherosclerosis is a multifactorial and progressive disease commonly correlated with a high fat diet. The aim of this study was to identify potential biomarkers for the early diagnosis and monitoring of the progression of atherogenesis in apoE(-/-) mice using (1)H NMR-based metabonomics. The apoE(-/-) mice were split into four groups according to the duration of high fat feeding (0 w, 2 w, 4 w and 8 w), and each group possessed different pathological characteristics. Serum (1)H NMR-based metabonomics selectively captured the metabotypes that correlated with the degree of atherosclerosis, showing a time-dependent progression from the physiological to pathophysiological status. It was noted that changes in HDL, choline, taurine, glycine and glucose may be regarded as specific biomarkers of the early stage of atherosclerosis. With the progression of atherosclerosis, disorders in the metabolism of amino acids such as valine, alanine and methionine appeared when large atherosclerotic plaques existed. Multiple biochemical disorders involving lipid metabolism, energy and fatty acid metabolism were observed in the progression of atherosclerosis in apoE(-/-) mice. This study demonstrated that (1)H NMR-based metabonomics can provide biochemical information about the progression of atherogenesis and offer a non-invasive means to discover potential biomarkers for the onset and development of atherosclerosis. PMID: 25241798 [PubMed - indexed for MEDLINE]

Related Articles Phosphoproteomic profiling reveals IL6-mediated paracrine signaling within the Ewing sarcoma family of tumors. Mol Cancer Res. 2014 Dec;12(12):1740-54 Authors: Anderson JL, Titz B, Akiyama R, Komisopoulou E, Park A, Tap WD, Graeber TG, Denny CT Abstract UNLABELLED: Members of the Ewing sarcoma family of tumors (ESFT) contain tumor-associated translocations that give rise to oncogenic transcription factors, most commonly EWS/FLI1. EWS/FLI1 plays a dominant role in tumor progression by modulating the expression of hundreds of target genes. Here, the impact of EWS/FLI1 inhibition, by RNAi-mediated knockdown, on cellular signaling was investigated using mass spectrometry-based phosphoproteomics to quantify global changes in phosphorylation. This unbiased approach identified hundreds of unique phosphopeptides enriched in processes such as regulation of cell cycle and cytoskeleton organization. In particular, phosphotyrosine profiling revealed a large upregulation of STAT3 phosphorylation upon EWS/FLI1 knockdown. However, single-cell analysis demonstrated that this was not a cell-autonomous effect of EWS/FLI1 deficiency, but rather a signaling effect occurring in cells in which knockdown does not occur. Conditioned media from knockdown cells were sufficient to induce STAT3 phosphorylation in control cells, verifying the presence of a soluble factor that can activate STAT3. Cytokine analysis and ligand/receptor inhibition experiments determined that this activation occurred, in part, through an IL6-dependent mechanism. Taken together, the data support a model in which EWS/FLI1 deficiency results in the secretion of soluble factors, such as IL6, which activate STAT signaling in bystander cells that maintain EWS/FLI1 expression. Furthermore, these soluble factors were shown to protect against apoptosis. IMPLICATIONS: EWS/FLI1 inhibition results in a novel adaptive response and suggests that targeting the IL6/STAT3 signaling pathway may increase the efficacy of ESFT therapies. PMID: 25092916 [PubMed - indexed for MEDLINE]

A Comparative Metabolomic Evaluation of Behcet's Disease with Arthritis and Seronegative Arthritis Using Synovial Fluid. PLoS One. 2015;10(8):e0135856 Authors: Ahn JK, Kim S, Kim J, Hwang J, Kim KH, Cha HS Abstract Behcet's disease (BD) with arthritis is often confused with seronegative arthritis (SNA) because of shared clinical symptoms and the lack of definitive biomarkers for BD. To investigate possible metabolic patterns and potential biomarkers of BD with arthritis, metabolomic profiling of synovial fluid (SF) from 6 patients with BD with arthritis and 18 patients with SNA was performed using gas chromatography/time-of-flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. A total of 123 metabolites were identified from samples. Orthogonal partial least square-discriminant analysis showed clear discrimination between BD with arthritis and SNA. A set of 11 metabolites were identified as potential biomarkers for BD using variable importance for projection values and the Wilcoxon-Mann-Whitney test. Compared with SNA, BD with arthritis exhibited relatively high levels of glutamate, valine, citramalate, leucine, methionine sulfoxide, glycerate, phosphate, lysine, isoleucine, urea, and citrulline. There were two markers identified, elevated methionine sulfoxide and citrulline, that were associated with increased oxidative stress, providing a potential link to BD-associated neutrophil hyperactivity. Glutamate, citramalate, and valine were selected and validated as putative biomarkers for BD with arthritis (sensitivity, 100%; specificity, 61.1%). This is the first report to present potential biomarkers from SF for discriminating BD with arthritis from SNA. The metabolomics of SF may be helpful in searching for potential biomarkers and elucidating the clinicopathogenesis of BD with arthritis. PMID: 26270538 [PubMed - as supplied by publisher]

Related Articles Coordinated activation of PTA-ACS and TCA cycles strongly reduces overflow metabolism of acetate in Escherichia coli. Appl Microbiol Biotechnol. 2014 Jun;98(11):5131-43 Authors: Peebo K, Valgepea K, Nahku R, Riis G, Oun M, Adamberg K, Vilu R Abstract Elimination of acetate overflow in aerobic cultivation of Escherichia coli would improve many bioprocesses as acetate accumulation in the growth environment leads to numerous negative effects, e.g. loss of carbon, inhibition of growth, target product synthesis, etc. Despite many years of studies, the mechanism and regulation of acetate overflow are still not completely understood. Therefore, we studied the growth of E. coli K-12 BW25113 and several of its mutant strains affecting acetate-related pathways using the continuous culture method accelerostat (A-stat) at various specific glucose consumption rates with the aim of diminishing acetate overflow. Absolute quantitative exo-metabolome and proteome analyses coupled to metabolic flux analysis enabled us to demonstrate that onset of acetate overflow can be postponed and acetate excretion strongly reduced in E. coli by coordinated activation of phosphotransacetylase-acetyl-CoA synthetase (PTA-ACS) and tricarboxylic acid (TCA) cycles. Fourfold reduction of acetate excretion (2 vs. 8 % from total carbon) at fastest growth compared to wild type was achieved by deleting the genes responsible for inactivation of acetyl-CoA synthetase protein (pka) and TCA cycle regulator arcA. The Δpka ΔarcA strain did not accumulate any other detrimental by-product besides acetate and showed identical μ max and only ~5 % lower biomass yield compared to wild type. We conclude that a fine-tuned coordination between increasing the recycling capabilities of acetate in the PTA-ACS node through a higher concentration of active acetate scavenging Acs protein and downstream metabolism throughput in the TCA cycle is necessary for diminishing overflow metabolism of acetate in E. coli and achieving higher target product production in bioprocesses. PMID: 24633370 [PubMed - indexed for MEDLINE]

Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects Book. 2015 Authors: Kobeissy FH Abstract There are four biochemical components that control biological systems by serving as building blocks and as information databases: genes, transcripts, proteins, and metabolites. The study of these four components have become entire fields of biological study and have often been referred to collectively as the omics, including genomics, transcriptomics, proteomics, and metabolomics. The ability to study each of these biological components in great detail and to study the relationship between them has led to significant advances in medical discovery and understanding. The goal of medical systems biology is to integrate all biological information to understand mechanistic information about cellular events and functions that may contribute to disease propensity, development, progression, diagnosis, and/or treatment. Having a systems perspective on human biology is desirable, where details of various system components can be integrated with increasing complexity to better understand properties of the entire system. The systems-oriented approach requires extensive and complex datasets; reliable analytical techniques; thoughtful data integration across platforms; and advanced biostatistical methods. Medical systems biology necessitates an unbiased and comprehensive approach when interpreting experimental results and biological interpretations need to be carefully explained, justified by the data, and tested on larger data sets. Traumatic brain injury (TBI) patients would benefit from a medical systems biology understanding of the systemic dysregulation and cellular changes that follow an insult to the head. A subspecialty in the critical care environment, neurocritical care, evolved from the acceptance that recovery from the primary injury to the brain tissue is affected by systemic alterations that can result in secondary injuries to the brain. The neurological intensive care unit (ICU) has realized significant improvements in patient outcomes due to protocols to address and prevent secondary injuries and due to neurointensivist-led teamwork, both aided by modern technological advances in multimodality neuromonitoring (Elf et al., 2002, Le Roux et al., 2012, Varelas et al., 2006). Considering the notable advances achieved through incorporating a systems-level approach to treating head injury and improving outcomes, in this review we discuss metabolomics applied to TBI. First, we will introduce metabolomics for readers not familiar with the field. Second, we summarize research on the metabolic changes following TBI to highlight what information has been translated to the clinic and what treatments exist. Finally, we discuss metabolomics techniques applied to TBI metabolism, reviewing the examples in the literature, and offering the authors’ suggestions for using NMR spectroscopy to study biofluids from head injured patients. As researchers and clinicians report and validate metabolomics findings, building a medical systems biology perspective on post-TBI metabolic dysfunction is likely to aid in informing physicians’ decisions and in integrating treatments into daily practice. Metabolomics refers to the study of the metabolome, which has been defined as “the quantitative complement of metabolites in a biological system” (Dunn et al., 2011). A metabolome, estimated to contain thousands of compounds, is organism-specific and sample type–specific. The human serum metabolome has been reported to contain 4,229 unique compounds, detection of which involved the use of several analytic techniques, and is still not considered exhaustive (Psychogios et al., 2011). Metabolomics studies aim to discriminate pathological metabolic profiles from that of a normal physiological state and to predict class assignment based on this set of metabolite biomarkers (Baker, 2011; Holmes et al., 2008; Nicholson et al., 2012). The field of metabolomics research consists of several investigative methods. First, there is a distinction to be made between targeted and exploratory metabolomics studies (Lenz and Wilson, 2007). In the latter, the goal is to generate a metabolomic fingerprint for each case and to use multivariate analysis to probe class-specific patterns. Generally, the focus of such studies is not to identify and quantify metabolites nor to propose mechanistic explanations of the results, but rather to predict class assignment based on the metabolomic fingerprint. Targeted metabolomics studies aim to identify and quantify specific metabolites. These metabolites may be hypothesized to be biomarkers of disease progression or may be considered an indicator of the severity of a physiological state. Targeted metabolomics studies may use the same multivariate statistical techniques as the metabolome fingerprint-type studies, but also typically include more traditional univariate and multivariate analyses on the metabolite concentrations. Targeted studies can be targeted to a set of endogenous metabolites or can be targeted to study an exogenous substance, including labeled tracer metabolites or a pharmaceutical. Blood plasma, blood serum, urine, and cerebrospinal fluid (CSF) have been extensively investigated in the metabolomics literature. These biofluids are readily available and are interpreted as an average representation of the surrounding tissue. Researchers working with animal models have access to tissue after sacrifice, which is considerably rarer in human studies. As the field has grown, online metabolite databases containing biological, structural, and experimental information have been developed and are a key tool for metabolomics researchers (Ulrich et al., 2008; Wishart et al., 2007). The term metabolomics resulted from research in the 1980s and 1990s (Nicholson et al., 1999), yet the concept behind metabolomics was a focus of research for several decades prior. What distinguishes contemporary metabolomics studies from past studies on metabolic changes is the technology available for analyzing such biofluid samples and, therefore, the extent and accuracy of the metabolome quantified. In addition to the larger data set, there have also been computational and statistical advances that make the prospect of drawing meaningful conclusions from thousands of metabolites and the changes that occur between classes possible. With improvements in technology, metabolomics research has reached a level of complexity requiring a multidisciplinary team and has made providing biological rationale for the findings challenging because of data set complexity. The Institute of Medicine of the National Academies published a report on translational omics that issued recommendations for improving the overall quality of the metabolomics research and for translating these findings to the clinical setting (Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, 2012). The use of mass spectrometry (MS)-based and nuclear magnetic resonance (NMR)-based quantification are the most common in the metabolomics literature. Both of these analytical instruments are reliable, accurate, and widely available. There are advantages and disadvantages associated with each, some of which will be briefly mentioned, and the reader is referred to a number of excellent metabolomics review articles (Dunn et al., 2011; Lenz and Wilson, 2007; Nicholson et al., 1999). Because an individual’s metabolome is highly influenced by environment and diet, population studies require a large number of subjects, and the reliability and reproducibility of these analytical techniques is key. The focus of this review is NMR-based metabolomics applied to TBI, but both analytical methods will be described. The reader is referred to extensive review articles focused on the application of MS and/or NMR to metabolomics (Dettmer et al., 2007; Zhang et al., 2010). MS detects compounds in the picomolar concentration range that become ionized after injection into the mass spectrometer; the readout is the mass-to-charge ratio of the detectable compounds in solution. MS-based metabolomics have used gas chromatography MS and liquid chromatography MS. Preparing samples for MS analysis requires extraction of metabolites and may require derivitization, which can be a labor-intensive process. Metabolite extraction involves a series of experimental steps in which metabolite loss can occur and where additional sample-to-sample variability may be introduced. The high sensitivity of MS-based quantification makes it a powerful tool in targeted metabolomics studies. In metabolome fingerprinting studies, it is challenging to measure all compounds with the same efficiency and accuracy for technical reasons. NMR spectroscopy is used to identify and quantify compounds in solution containing elements that are magnetic resonance–detectable (i.e., elemental isotopes that will absorb photons when placed in a magnetic field). NMR is considerably less sensitive than MS and is able to detect concentrations in the micromolar concentration range, but does not destroy the sample in the process of measurement. Application of a radiofrequency field at a known frequency and power excites the spin of the magnetic resonance–detectable isotopes. Spin is a fundamental property of elements akin to mass and charge and both the absorption and emission of radiofrequency photons is nondestructive and noninvasive. Each unique chemical structure in a molecule will resonate in the magnetic field at a specific frequency as the spins relax to equilibrium alignment with the magnetic field. The signal collected by the NMR spectrometer is then Fourier transformed into a NMR spectrum with spectral peaks at specific frequencies corresponding to the molecular structure of the compound being measured. The integrated area of the spectral peaks is proportional to the concentration of the compound. All compounds in solution above a certain concentration will be detected, unlike the variable efficiency of MS-based quantification. There is minimal sample preparation required when compared with MS. There are a number of biologically relevant isotopes that can be measured, including (1)H, (13)C, (31)P, and (15)N. (1)H is the most abundant isotope of hydrogen (99.99%) and, because biologically relevant molecules contain hydrogen, (1)H NMR is widely used. NMR spectrometers are standard equipment in research environments and increased spectral resolution is possible due to the prevalence of high-field spectrometers with field strengths ≥400 MHz (9.4 T). High-resolution magic angle spinning spectroscopy is able to quantify metabolites in intact tissue using solid-state NMR spectrometers (Beckonert et al., 2010). Another aspect of modern metabolomics research is application of multivariate statistical approaches. Unsupervised multivariate techniques such as principal component analysis (PCA) reduce the number of variables to a few principal components. Principal components are orthogonal to one another, are linear combinations of the original data, and can reduce hundreds of input variables to three or four. There are many NMR-based metabolomics fingerprint-type studies that use the complete NMR spectrum as the set of variables. Some metabolomics studies are designed to build a prediction model with supervised multivariate techniques, for example partial least squares (PLS) or PLS-discriminant analysis (PLS-DA) among others (Bylesjo et al., 2006). Most metabolomics studies generate a PCA model of the data to test whether the groups can be reasonably separated based on metabolic information. To build a predictive model, validation is vital and the data set is randomly separated into a larger training set and a smaller test set; the model generated from the training set is then tested on the test set. In reality, metabolomics studies generally quantify fewer than 100 metabolites per sample. Several advances are required to achieve high-throughput quantification of the entire metabolome and to translate metabolomics to the clinical setting. The steps following data collection, including processing and statistical analyses, will be discussed later in this chapter within the context of metabolomics of TBI. PMID: 26269925

The Metabolome in Finnish Carriers of the MYBPC3-Q1061X Mutation for Hypertrophic Cardiomyopathy. PLoS One. 2015;10(8):e0134184 Authors: Jørgenrud B, Jalanko M, Heliö T, Jääskeläinen P, Laine M, Hilvo M, Nieminen MS, Laakso M, Hyötyläinen T, Orešič M, Kuusisto J Abstract AIMS: Mutations in the cardiac myosin-binding protein C gene (MYBPC3) are the most common genetic cause of hypertrophic cardiomyopathy (HCM) worldwide. The molecular mechanisms leading to HCM are poorly understood. We investigated the metabolic profiles of mutation carriers with the HCM-causing MYBPC3-Q1061X mutation with and without left ventricular hypertrophy (LVH) and non-affected relatives, and the association of the metabolome to the echocardiographic parameters. METHODS AND RESULTS: 34 hypertrophic subjects carrying the MYBPC3-Q1061X mutation, 19 non-hypertrophic mutation carriers and 20 relatives with neither mutation nor hypertrophy were examined using comprehensive echocardiography. Plasma was analyzed for molecular lipids and polar metabolites using two metabolomics platforms. Concentrations of branched chain amino acids, triglycerides and ether phospholipids were increased in mutation carriers with hypertrophy as compared to controls and non-hypertrophic mutation carriers, and correlated with echocardiographic LVH and signs of diastolic and systolic dysfunction in subjects with the MYBPC3-Q1061X mutation. CONCLUSIONS: Our study implicates the potential role of branched chain amino acids, triglycerides and ether phospholipids in HCM, as well as suggests an association of these metabolites with remodeling and dysfunction of the left ventricle. PMID: 26267065 [PubMed - as supplied by publisher]

Analytical Bias in the Measurement of Serum 25-Hydroxyvitamin D Concentrations Impairs Assessment of Vitamin D Status in Clinical and Research Settings. PLoS One. 2015;10(8):e0135478 Authors: Black LJ, Anderson D, Clarke MW, Ponsonby AL, Lucas RM, Ausimmune Investigator Group Abstract Measured serum 25-hydroxyvitamin D concentrations vary depending on the type of assay used and the specific laboratory undertaking the analysis, impairing the accurate assessment of vitamin D status. We investigated differences in serum 25-hydroxyvitamin D concentrations measured at three laboratories (laboratories A and B using an assay based on liquid chromatography-tandem mass spectrometry and laboratory C using a DiaSorin Liaison assay), against a laboratory using an assay based on liquid chromatography-tandem mass spectrometry that is certified to the standard reference method developed by the National Institute of Standards and Technology and Ghent University (referred to as the 'certified laboratory'). Separate aliquots from the same original serum sample for a subset of 50 participants from the Ausimmune Study were analysed at the four laboratories. Bland-Altman plots were used to visually check agreement between each laboratory against the certified laboratory. Compared with the certified laboratory, serum 25-hydroxyvitamin D concentrations were on average 12.4 nmol/L higher at laboratory A (95% limits of agreement: -17.8,42.6); 12.8 nmol/L higher at laboratory B (95% limits of agreement: 0.8,24.8); and 10.6 nmol/L lower at laboratory C (95% limits of agreement: -48.4,27.1). The prevalence of vitamin D deficiency (defined here as 25-hydroxyvitamin D <50 nmol/L) was 24%, 16%, 12% and 41% at the certified laboratory, and laboratories A, B, and C, respectively. Our results demonstrate considerable differences in the measurement of 25-hydroxyvitamin D concentrations compared with a certified laboratory, even between laboratories using assays based on liquid chromatography-tandem mass spectrometry, which is often considered the gold-standard assay. To ensure accurate and reliable measurement of serum 25-hydroxyvitamin D concentrations, all laboratories should use an accuracy-based quality assurance system and, ideally, comply with international standardisation efforts. PMID: 26266807 [PubMed - as supplied by publisher]

Evidence for Chronic Kidney Disease-Mineral and Bone Disorder Associated With Metabolic Pathway Changes. Medicine (Baltimore). 2015 Aug;94(32):e1273 Authors: Wu Q, Lai X, Zhu Z, Hong Z, Dong X, Wang T, Wang H, Lou Z, Lin Q, Guo Z, Chai Y Abstract Abnormalities in the levels of calcium, phosphorus, and parathyroid hormone (PTH) in serum are typical for patients with chronic kidney disease (CKD). They are used routinely to predict the onset of CKD-mineral and bone disorder (MBD). However, CKD-MBD associated with metabolic pathway imbalance is not well understood.The objective of the study was to identify endogenous metabolic signatures in patients with intact PTH using mass spectrometry-based metabolomics. This study was a cross-sectional study. Ultra performance liquid chromatography-Quadrupole Time-of-Flight/mass spectrometry-based metabolic profiling was employed to analyze serum samples from 19 disease controls (DCs) (intact parathyroid hormone [iPTH] 150-300 pg/mL) and 19 secondary hyperparathyroidism (SHPT) patients (iPTH >300 pg/mL) (the training data set) to identify metabolic biomarkers for CKD-MBD. Then, another set of samples including 19 DCs (iPTH 150-300 pg/mL) and 19 SHPT patients (iPTH >300 pg/mL) (the test data set) were used to validate the potential biomarkers identified.Metabolic profiling analyses revealed different patterns of endogenous metabolites between the SHPT and the DC groups. A total of 32 unique metabolites were identified and 30 metabolites were elevated in the iPTH compared with control serum pools. Cytidine and L-phenylalanine were downregulated in the SHPT patients. The metabolic signatures identified were assessed respectively by an internal 10-fold cross validation with an accuracy of 91.4% and an external validation with an accuracy of 71.1%, a sensitivity of 73.7%, and a specificity of 68.4%.Mass spectrometry-based metabolomic analyses for SHPT patients promises immense potential for early diagnosis and therapy monitoring. Our results indicated that the onset of CKD-MBD is associated with pathway changes of protein synthesis and metabolism, amino acid metabolism, energy metabolism, and steroid hormone metabolism, with obvious promise for better understanding the pathogenesis of this disease. Several metabolic biomarkers were identified, which warrant further development. PMID: 26266360 [PubMed - as supplied by publisher]

A metabolomics strategy to explore urinary biomarkers and metabolic pathways for assessment of interaction between Danhong injection and low-dose aspirin during their synergistic treatment. J Chromatogr B Analyt Technol Biomed Life Sci. 2015 Jul 26; Authors: Li J, Guo J, Shang E, Zhu Z, Zhu KY, Li S, Zhao B, Jia L, Zhao J, Tang Z, Duan J Abstract The drug combination of Danhong injection (DHI) and low-dose aspirin (ASA) was frequently applied for the treatment of cardiovascular and cerebrovascular diseases. Due to the drug interactions, a lot of potential benefits and risks might exist side by side in the course of combination therapy. However, there had been no studies of interaction between DHI and ASA. Metabolomics was a powerful tool to explore endogenous biomarkers and metabolic pathways. In present study, metabolic profiling with ultra-high-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UHPLC-QTOF/MS) coupled with multivariate statistical analysis was performed to provide insight into understanding the interaction between DHI and low-dose ASA. Eleven potential biomarkers of three types were identified and seven metabolic pathways were constructed. The results showed that the interaction between DHI and low-dose ASA during synergistic treatment indeed affected some key endogenous biomarkers and metabolic pathways, which could not happen when DHI or low-dose ASA was used alone. The quality and quantity of endogenous metabolite were both influenced by interaction between DHI and low-dose ASA. In details, the amount of flavin mononucleotide, L-2, 4-diaminobutyric acid (DABA) and 4-aminohippuric acid were significantly increased. On the contrary, the amount of 3-methyluridine, 4, 6-dihydroxyquinoline, cortolone-3-glucuronide, and serotonin were significantly decreased. Furthermore, O-phosphotyrosine, 3-methyl-2-butenal, indoxyl sulfate and dolichyl diphosphate were disappeared in urine. As to metabolic pathways, riboflavin metabolism, pentose and glucuronate interconversions, and tryptophan metabolism were all significantly influenced. The emerging alterations of biomarkers and metabolic pathways were associated with a lot of drugs and diseases based on literature researches, which might influence the co-administration of other drugs or the treatments of relevant diseases. Our paper presented some hints to uncover the mechanism of interaction between DHI and low-dose ASA, which would provide some references for application of DHI and low-dose ASA combination. PMID: 26265434 [PubMed - as supplied by publisher]

Profiling the metabolome changes caused by cranberry procyanidins in plasma of female rats using H NMR and UHPLC-Q-Orbitrap-HRMS global metabolomics approaches. Mol Nutr Food Res. 2015 Aug 12; Authors: Liu H, Garrett TJ, Tayyari F, Gu L Abstract SCOPE: The objective was to investigate the metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) using (1) H NMR and UHPLC-Q-Orbitrap-HRMS metabolomics approaches, and to identify the contributing metabolites. METHODS AND RESULTS: Twenty four female Sprague-Dawley rats were randomly separated into two groups and administered PPCP or partially purified apple procyanidins (PPAP) for 3 times using a 250 mg extracts/extracts body weight dose. Plasma were collected six hours after the last gavage and analyzed using (1) H NMR and UHPLC-Q-Orbitrap-HRMS. No metabolome difference was observed using (1) H NMR metabolomics approach. However, LC-HRMS metabolomics data show that metabolome in plasma of female rats administered PPCP differed from those gavaged with PPAP. Eleven metabolites were tentatively identified from a total of 36 discriminant metabolic features based on accurate masses and/and product ion spectra. PPCP caused a greater increase of exogenous metabolites including p-hydroxybenzoic acid, phenol, phenol-sulfate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, and 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide in rat plasma. Furthermore, the plasma level of O-methyl-(-)-epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(hydroxyphenyl)-ϒ-valerolactone-O-sulphate, 4-hydroxydiphenylamine, and peonidin-3-O-hexose were higher in female rats administered with PPAP. CONCLUSION: The metabolome changes caused by cranberry procyanidins were revealed using an UHPLC-Q-Orbitrap-HRMS global metabolomics approach. Exogenous and microbial metabolites were the major identified discriminate biomarkers. This article is protected by copyright. All rights reserved. PMID: 26264887 [PubMed - as supplied by publisher]

Discovery of urinary biomarkers of whole grain rye intake in free-living subjects using non-targeted LC-MS metabolite profiling (1-9). Mol Nutr Food Res. 2015 Aug 12; Authors: Hanhineva K, Brunius C, Andersson A, Marklund M, Juvonen R, Keski-Rahkonen P, Auriola S, Landberg R Abstract SCOPE: Whole grain (WG) intake is associated with decreased risk of developing colorectal cancer, type 2 diabetes and cardiovascular disease and its comorbidities. However, the role of specific grains is unclear. Moreover, intake of specific WG is challenging to measure accurately with traditional dietary assessment methods. Our aim was to use non-targeted metabolite profiling to discover specific urinary biomarkers for WG rye to objectively reflect intake under free-living conditions. METHODS AND RESULTS: WG rye intake was estimated by weighed food records, and 24 h urine collections were analyzed by LC-MS. Multivariate modelling was undertaken by repeated double cross-validated partial least squares regression against reported WG rye intake, which correlated well with multivariate prediction estimates (r = 0.67-0.80, p<0.001), but not with intakes of WG wheat or oats. Hydroxyhydroxyphenyl acetamide (HHPA) sulfate, 3,5-dihydroxyphenylpropionic acid (DHPPA) sulfate, caffeic acid sulfate and hydroxyphenyl acetamide (HPAA) sulfate were among the 20 features which had the greatest potential as intake biomarkers of WG. In addition, three compounds exhibited MS/MS fragmentation of carnitine structures. CONCLUSION: With this non-targeted approach, we confirmed the specificity of alkylresorcinol metabolites as biomarkers for WG rye intake, but also discovered other compounds that should be evaluated as putative biomarkers in future studies. This article is protected by copyright. All rights reserved. PMID: 26264776 [PubMed - as supplied by publisher]

Related Articles Sampling and analysis of metabolomes in biological fluids. Analyst. 2014 Aug 7;139(15):3683-94 Authors: Nunes de Paiva MJ, Menezes HC, de Lourdes Cardeal Z Abstract Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents. Qualitative and quantitative metabolome analyses are used for the diagnosis of various diseases or chemical exposure. This article presents an overview of the different analytical methods available for performing the determination of the metabolome, including sampling, sample preparation and processing and interpretation of data. Critical comments are aimed at emphasizing the extraction methods as well as the biological samples used for metabolome analysis and data processing. PMID: 24941103 [PubMed - indexed for MEDLINE]

Related Articles Metabolic signatures associated with environmental pollution by metals in Doñana National Park using P. clarkii as bioindicator. Environ Sci Pollut Res Int. 2014 Dec;21(23):13315-23 Authors: Gago-Tinoco A, González-Domínguez R, García-Barrera T, Blasco-Moreno J, Bebianno MJ, Gómez-Ariza JL Abstract Bioindicators can reflect the effects of pollutants on their metabolism, being widely used to assess environmental stress. In this sense, the crab Procambarus clarkii has been previously proposed to monitor the contamination in Doñana National Park (southwest Spain) using conventional biomarkers. In this work, a metabolomic approach based on direct infusion mass spectrometry, which allows an easy and quick study of a large number of metabolites in a single run, was used for pollution assessment of this area, considering the biological response of this organism to contamination. In addition, metal accumulation in crab tissues was determined. Thus, the integrated analysis of metabolomic and metallomic data enabled the study of metabolic response of the organism against pollution. Several metabolites were discovered as potential biomarkers of pollution, such as decreased levels of carnosine, alanine, niacinamide, acetoacetate, pantothenic acid, ascorbate, glucose-6-phosphate, arginine, glucose, lactate, phospholipids, and tryglicerides, as well as elevated levels of acetyl carnitine, phosphocholine, choline, and uric acid. In this way, metal-induced toxicity could be related to metabolic impairments, principally oxidative stress, metabolic dysfunction, and dyslipidemia. PMID: 24756666 [PubMed - indexed for MEDLINE]