An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry.

PubMed

Wang, Yang; Liu, Fang; Li, Peng; He, Chengwei; Wang, Ruibing; Su, Huanxing; Wan, Jian-Bo

2016-07-13

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies. Copyright © 2016 Elsevier B.V. All rights reserved.

Metabolomics for Biomarker Discovery in Gastroenterological Cancer

PubMed Central

Nishiumi, Shin; Suzuki, Makoto; Kobayashi, Takashi; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

2014-01-01

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis. PMID:25003943

NMR-based Metabolomics for Cancer Research

EPA Science Inventory

Metabolomics is considered as a complementary tool to other omics platforms to provide a snapshot of the cellular biochemistry and physiology taking place at any instant. Metabolmics approaches have been widely used to provide comprehensive and quantitative analyses of the metabo...

Shotgun metabolomic approach based on mass spectrometry for hepatic mitochondria of mice under arsenic exposure.

PubMed

García-Sevillano, M A; García-Barrera, T; Navarro, F; Montero-Lobato, Z; Gómez-Ariza, J L

2015-04-01

Mass spectrometry (MS)-based toxicometabolomics requires analytical approaches for obtaining unbiased metabolic profiles. The present work explores the general application of direct infusion MS using a high mass resolution analyzer (a hybrid systems triple quadrupole-time-of-flight) and a complementary gas chromatography-MS analysis to mitochondria extracts from mouse hepatic cells, emphasizing on mitochondria isolation from hepatic cells with a commercial kit, sample treatment after cell lysis, comprehensive metabolomic analysis and pattern recognition from metabolic profiles. Finally, the metabolomic platform was successfully checked on a case-study based on the exposure experiment of mice Mus musculus to inorganic arsenic during 12 days. Endogenous metabolites alterations were recognized by partial least squares-discriminant analysis. Subsequently, metabolites were identified by combining MS/MS analysis and metabolomics databases. This work reports for the first time the effects of As-exposure on hepatic mitochondria metabolic pathways based on MS, and reveals disturbances in Krebs cycle, β-oxidation pathway, amino acids degradation and perturbations in creatine levels. This non-target analysis provides extensive metabolic information from mitochondrial organelle, which could be applied to toxicology, pharmacology and clinical studies.

NMR-based metabolomics approach to study the chronic toxicity of crude ricin from castor bean kernels on rats.

PubMed

Guo, Pingping; Wang, Junsong; Dong, Ge; Wei, Dandan; Li, Minghui; Yang, Minghua; Kong, Lingyi

2014-07-29

Ricin, a large, water soluble toxic glycoprotein, is distributed majorly in the kernels of castor beans (the seeds of Ricinus communis L.) and has been used in traditional Chinese medicine (TCM) or other folk remedies throughout the world. The toxicity of crude ricin (CR) from castor bean kernels was investigated for the first time using an NMR-based metabolomic approach complemented with histopathological inspection and clinical chemistry. The chronic administration of CR could cause kidney and lung impairment, spleen and thymus dysfunction and diminished nutrient intake in rats. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) of metabolomic profiles of rat biofluids highlighted a number of metabolic disturbances induced by CR. Long-term CR treatment produced perturbations on energy metabolism, nitrogen metabolism, amino acid metabolism and kynurenine pathway, and evoked oxidative stress. These findings could explain well the CR induced nephrotoxicity and pulmonary toxicity, and provided several potential biomarkers for diagnostics of these toxicities. Such a (1)H NMR based metabolomics approach showed its ability to give a systematic and holistic view of the response of an organism to drugs and is suitable for dynamic studies on the toxicological effects of TCM.

Metabolite profiling of fish skin mucus: a novel approach for minimally-invasive environmental exposure monitoring and surveillance.

PubMed

Ekman, D R; Skelton, D M; Davis, J M; Villeneuve, D L; Cavallin, J E; Schroeder, A; Jensen, K M; Ankley, G T; Collette, T W

2015-03-03

The application of 'omics tools to biologically based monitoring and surveillance of aquatic environments shows considerable promise for complementing chemical monitoring in ecological risk assessments. However, few of the current approaches offer the ability to sample ecologically relevant species (e.g., fish) in a way that produces minimal impact on the health of the organism(s) under study. In the current study we employ liquid chromatography tandem mass spectrometry (LC-MS/MS) to assess the potential for skin mucus-based metabolomics for minimally invasive sampling of the fathead minnow (FHM; Pimephales promelas). Using this approach we were able to detect 204 distinct metabolites in the FHM skin mucus metabolome representing a large number of metabolite classes. An analysis of the sex specificity of the skin mucus metabolome showed it to be highly sexually dimorphic with 72 of the detected metabolites showing a statistically significant bias with regard to sex. Finally, in a proof-of-concept fashion we report on the use of skin mucus-based metabolomics to assess exposures in male and female fathead minnows to an environmentally relevant concentration of bisphenol A, a nearly ubiquitous environmental contaminant and an established endocrine active chemical.

(1)H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors.

PubMed

Vázquez-Fresno, Rosa; Llorach, Rafael; Alcaro, Francesca; Rodríguez, Miguel Ángel; Vinaixa, Maria; Chiva-Blanch, Gemma; Estruch, Ramon; Correig, Xavier; Andrés-Lacueva, Cristina

2012-08-01

Moderate wine consumption is associated with health-promoting activities. An H-NMR-based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty-one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24-h urine samples were collected and analyzed by (1) H-NMR. According to the results of a one-way ANOVA, significant markers were grouped in four categories: alcohol-related markers (ethanol); gin-related markers; wine-related markers; and gut microbiota markers (hippurate and 4-hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched-chain amino acid (BCAA) metabolite (3-methyl-oxovalerate). Additionally, a possible interaction between alcohol and gut-related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

PubMed Central

Vernocchi, Pamela; Del Chierico, Federica; Putignani, Lorenza

2016-01-01

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies. PMID:27507964

Pre-analytic evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow.

PubMed

Volani, Chiara; Caprioli, Giulia; Calderisi, Giovanni; Sigurdsson, Baldur B; Rainer, Johannes; Gentilini, Ivo; Hicks, Andrew A; Pramstaller, Peter P; Weiss, Guenter; Smarason, Sigurdur V; Paglia, Giuseppe

2017-10-01

Volumetric absorptive microsampling (VAMS) is a novel approach that allows single-drop (10 μL) blood collection. Integration of VAMS with mass spectrometry (MS)-based untargeted metabolomics is an attractive solution for both human and animal studies. However, to boost the use of VAMS in metabolomics, key pre-analytical questions need to be addressed. Therefore, in this work, we integrated VAMS in a MS-based untargeted metabolomics workflow and investigated pre-analytical strategies such as sample extraction procedures and metabolome stability at different storage conditions. We first evaluated the best extraction procedure for the polar metabolome and found that the highest number and amount of metabolites were recovered upon extraction with acetonitrile/water (70:30). In contrast, basic conditions (pH 9) resulted in divergent metabolite profiles mainly resulting from the extraction of intracellular metabolites originating from red blood cells. In addition, the prolonged storage of blood samples at room temperature caused significant changes in metabolome composition, but once the VAMS devices were stored at - 80 °C, the metabolome remained stable for up to 6 months. The time used for drying the sample did also affect the metabolome. In fact, some metabolites were rapidly degraded or accumulated in the sample during the first 48 h at room temperature, indicating that a longer drying step will significantly change the concentration in the sample. Graphical abstract Volumetric absorptive microsampling (VAMS) is a novel technology that allows single-drop blood collection and, in combination with mass spectrometry (MS)-based untargeted metabolomics, represents an attractive solution for both human and animal studies. In this work, we integrated VAMS in a MS-based untargeted metabolomics workflow and investigated pre-analytical strategies such as sample extraction procedures and metabolome stability at different storage conditions. The latter revealed that prolonged storage of blood samples at room temperature caused significant changes in metabolome composition, but if VAMS devices were stored at - 80 °C, the metabolome remained stable for up to 6 months.

Chemical Composition and Seasonality of Aromatic Mediterranean Plant Species by NMR-Based Metabolomics

PubMed Central

Scognamiglio, Monica; D'Abrosca, Brigida; Esposito, Assunta; Fiorentino, Antonio

2015-01-01

An NMR-based metabolomic approach has been applied to analyse seven aromatic Mediterranean plant species used in traditional cuisine. Based on the ethnobotanical use of these plants, the approach has been employed in order to study the metabolic changes during different seasons. Primary and secondary metabolites have been detected and quantified. Flavonoids (apigenin, quercetin, and kaempferol derivatives) and phenylpropanoid derivatives (e.g., chlorogenic and rosmarinic acid) are the main identified polyphenols. The richness in these metabolites could explain the biological properties ascribed to these plant species. PMID:25785229

Updates in metabolomics tools and resources: 2014-2015.

PubMed

Misra, Biswapriya B; van der Hooft, Justin J J

2016-01-01

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources--in the form of tools, software, and databases--is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A comparative UPLC-Q/TOF-MS-based metabolomics approach for distinguishing Zingiber officinale Roscoe of two geographical origins.

PubMed

Mais, Enos; Alolga, Raphael N; Wang, Shi-Lei; Linus, Loveth O; Yin, Xiaojin; Qi, Lian-Wen

2018-02-01

Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics.

PubMed

Gika, Helen G; Theodoridis, Georgios A; Plumb, Robert S; Wilson, Ian D

2014-01-01

Based on publication and citation numbers liquid chromatography (LC-MS) has become the major analytical technology in the field of global metabolite profiling. This dominance reflects significant investments from both the research community and instrument manufacturers. Here an overview of the approaches taken for LC-MS-based metabolomics research is given, describing critical steps in the realisation of such studies: study design and its needs, specific technological problems to be addressed and major obstacles in data treatment and biomarker identification. The current state of the art for LC-MS-based analysis in metabonomics/metabolomics is described including recent developments in liquid chromatography, mass spectrometry and data treatment as these are applied in metabolomics underlining the challenges, limitations and prospects for metabolomics research. Examples of the application of metabolite profiling in the life sciences focusing on disease biomarker discovery are highlighted. In addition, new developments and future prospects are described. Copyright © 2013 Elsevier B.V. All rights reserved.

Urinary metabolomic fingerprinting after consumption of a probiotic strain in women with mastitis.

PubMed

Vázquez-Fresno, Rosa; Llorach, Rafael; Marinic, Jelena; Tulipani, Sara; Garcia-Aloy, Mar; Espinosa-Martos, Irene; Jiménez, Esther; Rodríguez, Juan Miguel; Andres-Lacueva, Cristina

2014-09-01

Infectious mastitis is a common condition among lactating women, with staphylococci and streptococci being the main aetiological agents. In this context, some lactobacilli strains isolated from breast milk appear to be particularly effective for treating mastitis and, therefore, constitute an attractive alternative to antibiotherapy. A (1)H NMR-based metabolomic approach was applied to detect metabolomic differences after consuming a probiotic strain (Lactobacillus salivarius PS2) in women with mastitis. 24h urine of women with lactational mastitis was collected at baseline and after 21 days of probiotic (PB) administration. Multivariate analysis (OSC-PLS-DA and hierarchical clustering) showed metabolome differences after PB treatment. The discriminant metabolites detected at baseline were lactose, and ibuprofen and acetaminophen (two pharmacological drugs commonly used for mastitis pain), while, after PB intake, creatine and the gut microbial co-metabolites hippurate and TMAO were detected. In addition, a voluntary desertion of the pharmacological drugs ibuprofen and acetaminophen was observed after probiotic administration. The application of NMR-based metabolomics enabled the identification of the overall effects of probiotic consumption among women suffering from mastitis and highlighted the potential of this approach in evaluating the outcomes of probiotics consumption. To our knowledge, this is the first time that this approach has been applied in women with mastitis during lactation. Copyright © 2014. Published by Elsevier Ltd.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer.

PubMed

Lin, Yan; Ma, Changchun; Liu, Chengkang; Wang, Zhening; Yang, Jurong; Liu, Xinmu; Shen, Zhiwei; Wu, Renhua

2016-05-17

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

PubMed Central

Lin, Yan; Ma, Changchun; Liu, Chengkang; Wang, Zhening; Yang, Jurong; Liu, Xinmu; Shen, Zhiwei; Wu, Renhua

2016-01-01

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients. PMID:27107423

Mass Spectrometry-Based Metabolomics to Elucidate Functions in Marine Organisms and Ecosystems

PubMed Central

Goulitquer, Sophie; Potin, Philippe; Tonon, Thierry

2012-01-01

Marine systems are very diverse and recognized as being sources of a wide range of biomolecules. This review provides an overview of metabolite profiling based on mass spectrometry (MS) approaches in marine organisms and their environments, focusing on recent advances in the field. We also point out some of the technical challenges that need to be overcome in order to increase applications of metabolomics in marine systems, including extraction of chemical compounds from different matrices and data management. Metabolites being important links between genotype and phenotype, we describe added value provided by integration of data from metabolite profiling with other layers of omics, as well as their importance for the development of systems biology approaches in marine systems to study several biological processes, and to analyze interactions between organisms within communities. The growing importance of MS-based metabolomics in chemical ecology studies in marine ecosystems is also illustrated. PMID:22690147

Multi-platform metabolomics and a genetic approach support the authentication of agarwood produced by Aquilaria crassna and Aquilaria malaccensis.

PubMed

Nguyen, Huy Truong; Min, Jung-Eun; Long, Nguyen Phuoc; Thanh, Ma Chi; Le, Thi Hong Van; Lee, Jeongmi; Park, Jeong Hill; Kwon, Sung Won

2017-08-05

Agarwood, the resinous heartwood produced by some Aquilaria species such as Aquilaria crassna, Aquilaria malaccensis and Aquilaria sinensis, has been traditionally and widely used in medicine, incenses and especially perfumes. However, up to now, the authentication of agarwood has been largely based on morphological characteristics, a method which is prone to errors and lacks reproducibility. Hence, in this study, we applied metabolomics and a genetic approach to the authentication of two common agarwood chips, those produced by Aquilaria crassna and Aquilaria malaccensis. Primary metabolites, secondary metabolites and DNA markers of agarwood were authenticated by 1 H NMR metabolomics, GC-MS metabolomics and DNA-based techniques, respectively. The results indicated that agarwood chips could be classified accurately by all the methods illustrated in this study. Additionally, the pros and cons of each method are also discussed. To the best of our knowledge, our research is the first study detailing all the differences in the primary and secondary metabolites, as well as the DNA markers between the agarwood produced by these two species. Copyright © 2017 Elsevier B.V. All rights reserved.

Increasing rigor in NMR-based metabolomics through validated and open source tools

PubMed Central

Eghbalnia, Hamid R; Romero, Pedro R; Westler, William M; Baskaran, Kumaran; Ulrich, Eldon L; Markley, John L

2016-01-01

The metabolome, the collection of small molecules associated with an organism, is a growing subject of inquiry, with the data utilized for data-intensive systems biology, disease diagnostics, biomarker discovery, and the broader characterization of small molecules in mixtures. Owing to their close proximity to the functional endpoints that govern an organism’s phenotype, metabolites are highly informative about functional states. The field of metabolomics identifies and quantifies endogenous and exogenous metabolites in biological samples. Information acquired from nuclear magnetic spectroscopy (NMR), mass spectrometry (MS), and the published literature, as processed by statistical approaches, are driving increasingly wider applications of metabolomics. This review focuses on the role of databases and software tools in advancing the rigor, robustness, reproducibility, and validation of metabolomics studies. PMID:27643760

Increasing rigor in NMR-based metabolomics through validated and open source tools.

PubMed

Eghbalnia, Hamid R; Romero, Pedro R; Westler, William M; Baskaran, Kumaran; Ulrich, Eldon L; Markley, John L

2017-02-01

The metabolome, the collection of small molecules associated with an organism, is a growing subject of inquiry, with the data utilized for data-intensive systems biology, disease diagnostics, biomarker discovery, and the broader characterization of small molecules in mixtures. Owing to their close proximity to the functional endpoints that govern an organism's phenotype, metabolites are highly informative about functional states. The field of metabolomics identifies and quantifies endogenous and exogenous metabolites in biological samples. Information acquired from nuclear magnetic spectroscopy (NMR), mass spectrometry (MS), and the published literature, as processed by statistical approaches, are driving increasingly wider applications of metabolomics. This review focuses on the role of databases and software tools in advancing the rigor, robustness, reproducibility, and validation of metabolomics studies. Copyright © 2016. Published by Elsevier Ltd.

Structured plant metabolomics for the simultaneous exploration of multiple factors.

PubMed

Vasilev, Nikolay; Boccard, Julien; Lang, Gerhard; Grömping, Ulrike; Fischer, Rainer; Goepfert, Simon; Rudaz, Serge; Schillberg, Stefan

2016-11-17

Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor-metabolite crosstalk. However, unravelling all factor-metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset.

Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice.

PubMed

Zhang, Yan; Zhao, Fuzheng; Deng, Yongfeng; Zhao, Yanping; Ren, Hongqiang

2015-04-03

Disinfection byproducts (DBPs) in drinking water have been linked to various diseases, including colon, colorectal, rectal, and bladder cancer. Trichloroacetamide (TCAcAm) is an emerging nitrogenous DBP, and our previous study found that TCAcAm could induce some changes associated with host-gut microbiota co-metabolism. In this study, we used an integrated approach combining metagenomics, based on high-throughput sequencing, and metabolomics, based on nuclear magnetic resonance (NMR), to evaluate the toxic effects of TCAcAm exposure on the gut microbiome and urine metabolome. High-throughput sequencing revealed that the gut microbiome's composition and function were significantly altered after TCAcAm exposure for 90 days in Mus musculus mice. In addition, metabolomic analysis showed that a number of gut microbiota-related metabolites were dramatically perturbed in the urine of the mice. These results may provide novel insight into evaluating the health risk of environmental pollutants as well as revealing the potential mechanism of TCAcAm's toxic effects.

CE-MS for metabolomics: Developments and applications in the period 2014-2016.

PubMed

Ramautar, Rawi; Somsen, Govert W; de Jong, Gerhardus J

2017-01-01

CE-MS can be considered a useful analytical technique for the global profiling of (highly) polar and charged metabolites in various samples. Over the past few years, significant advancements have been made in CE-MS approaches for metabolomics studies. In this paper, which is a follow-up of a previous review paper covering the years 2012-2014 (Electrophoresis 2015, 36, 212-224), recent CE-MS strategies developed for metabolomics covering the literature from July 2014 to June 2016 are outlined. Attention will be paid to new CE-MS approaches for the profiling of anionic metabolites and the potential of SPE coupled to CE-MS is also demonstrated. Representative examples illustrate the applicability of CE-MS in the fields of biomedical, clinical, microbial, plant, and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, general conclusions and perspectives are given. © 2016 The Authors ELECTROPHORESIS Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics for comparison of caffeinated and decaffeinated coffee and its implications for Alzheimer's disease.

PubMed

Chang, Kai Lun; Ho, Paul C

2014-01-01

Findings from epidemiology, preclinical and clinical studies indicate that consumption of coffee could have beneficial effects against dementia and Alzheimer's disease (AD). The benefits appear to come from caffeinated coffee, but not decaffeinated coffee or pure caffeine itself. Therefore, the objective of this study was to use metabolomics approach to delineate the discriminant metabolites between caffeinated and decaffeinated coffee, which could have contributed to the observed therapeutic benefits. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics approach was employed to characterize the metabolic differences between caffeinated and decaffeinated coffee. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed distinct separation between the two types of coffee (cumulative Q(2) = 0.998). A total of 69 discriminant metabolites were identified based on the OPLS-DA model, with 37 and 32 metabolites detected to be higher in caffeinated and decaffeinated coffee, respectively. These metabolites include several benzoate and cinnamate-derived phenolic compounds, organic acids, sugar, fatty acids, and amino acids. Our study successfully established GC-TOF-MS based metabolomics approach as a highly robust tool in discriminant analysis between caffeinated and decaffeinated coffee samples. Discriminant metabolites identified in this study are biologically relevant and provide valuable insights into therapeutic research of coffee against AD. Our data also hint at possible involvement of gut microbial metabolism to enhance therapeutic potential of coffee components, which represents an interesting area for future research.

Gas Chromatography Time-Of-Flight Mass Spectrometry (GC-TOF-MS)-Based Metabolomics for Comparison of Caffeinated and Decaffeinated Coffee and Its Implications for Alzheimer’s Disease

PubMed Central

Chang, Kai Lun; Ho, Paul C.

2014-01-01

Findings from epidemiology, preclinical and clinical studies indicate that consumption of coffee could have beneficial effects against dementia and Alzheimer’s disease (AD). The benefits appear to come from caffeinated coffee, but not decaffeinated coffee or pure caffeine itself. Therefore, the objective of this study was to use metabolomics approach to delineate the discriminant metabolites between caffeinated and decaffeinated coffee, which could have contributed to the observed therapeutic benefits. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics approach was employed to characterize the metabolic differences between caffeinated and decaffeinated coffee. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed distinct separation between the two types of coffee (cumulative Q2 = 0.998). A total of 69 discriminant metabolites were identified based on the OPLS-DA model, with 37 and 32 metabolites detected to be higher in caffeinated and decaffeinated coffee, respectively. These metabolites include several benzoate and cinnamate-derived phenolic compounds, organic acids, sugar, fatty acids, and amino acids. Our study successfully established GC-TOF-MS based metabolomics approach as a highly robust tool in discriminant analysis between caffeinated and decaffeinated coffee samples. Discriminant metabolites identified in this study are biologically relevant and provide valuable insights into therapeutic research of coffee against AD. Our data also hint at possible involvement of gut microbial metabolism to enhance therapeutic potential of coffee components, which represents an interesting area for future research. PMID:25098597

Metabolomics as a tool in the identification of dietary biomarkers.

PubMed

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Mass Spectrometry-Based Metabolomic and Proteomic Strategies in Organic Acidemias

PubMed Central

Imperlini, Esther; Santorelli, Lucia; Orrù, Stefania; Scolamiero, Emanuela; Ruoppolo, Margherita

2016-01-01

Organic acidemias (OAs) are inherited metabolic disorders caused by deficiency of enzymatic activities in the catabolism of amino acids, carbohydrates, or lipids. These disorders result in the accumulation of mono-, di-, or tricarboxylic acids, generally referred to as organic acids. The OA outcomes can involve different organs and/or systems. Some OA disorders are easily managed if promptly diagnosed and treated, whereas, in others cases, such as propionate metabolism-related OAs (propionic acidemia, PA; methylmalonic acidemia, MMA), neither diet, vitamin therapy, nor liver transplantation appears to prevent multiorgan impairment. Here, we review the recent developments in dissecting molecular bases of OAs by using integration of mass spectrometry- (MS-) based metabolomic and proteomic strategies. MS-based techniques have facilitated the rapid and economical evaluation of a broad spectrum of metabolites in various body fluids, also collected in small samples, like dried blood spots. This approach has enabled the timely diagnosis of OAs, thereby facilitating early therapeutic intervention. Besides providing an overview of MS-based approaches most frequently used to study the molecular mechanisms underlying OA pathophysiology, we discuss the principal challenges of metabolomic and proteomic applications to OAs. PMID:27403441

Distinct urine metabolome after Asian ginseng and American ginseng intervention based on GC-MS metabolomics approach

PubMed Central

Yang, Liu; Yu, Qing-Tao; Ge, Ya-Zhong; Zhang, Wen-Song; Fan, Yong; Ma, Chung-Wah; Liu, Qun; Qi, Lian-Wen

2016-01-01

Ginseng occupies a prominent position in the list of best-selling natural products worldwide. Asian ginseng (Panax ginseng) and American ginseng (Panax quinquefolius) show different properties and medicinal applications in pharmacology, even though the main active constituents of them are both thought to be ginsenosides. Metabolomics is a promising method to profile entire endogenous metabolites and monitor their fluctuations related to exogenous stimulus. Herein, an untargeted metabolomics approach was applied to study the overall urine metabolic differences between Asian ginseng and American ginseng in mice. Metabolomics analyses were performed using gas chromatography-mass spectrometry (GC-MS) together with multivariate statistical data analysis. A total of 21 metabolites related to D-glutamine and D-glutamate metabolism, glutathione metabolism, TCA cycle and glyoxylate and dicarboxylate metabolism, differed significantly under the Asian ginseng treatment; 34 metabolites mainly associated with glyoxylate and dicarboxylate metabolism, TCA cycle and taurine and hypotaurine metabolism, were significantly altered after American ginseng treatment. Urinary metabolomics reveal that Asian ginseng and American ginseng can benefit organism physiological and biological functions via regulating multiple metabolic pathways. The important pathways identified from Asian ginseng and American ginseng can also help to explore new therapeutic effects or action targets so as to broad application of these two ginsengs. PMID:27991533

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.

PubMed

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-12-13

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental &biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

PubMed Central

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-01-01

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data. PMID:27958387

Mathematical Modeling Approaches in Plant Metabolomics.

PubMed

Fürtauer, Lisa; Weiszmann, Jakob; Weckwerth, Wolfram; Nägele, Thomas

2018-01-01

The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.

Nutritional metabolomics: Progress in addressing complexity in diet and health

PubMed Central

Jones, Dean P.; Park, Youngja; Ziegler, Thomas R.

2013-01-01

Nutritional metabolomics is rapidly maturing to use small molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health and complex pathobiology, serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and non-targeted metabolic profiling, along with bioinformatics, pathway mapping and computational modeling, are now used for nutrition research on diet, metabolism, microbiome and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics and health phenotyping, to support integrated models required for personalized diet and nutrition forecasting. PMID:22540256

New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE).

PubMed

Sanchon-Lopez, Beatriz; Everett, Jeremy R

2016-09-02

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.

Mass spectrometry-based metabolomics: applications to biomarker and metabolic pathway research.

PubMed

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2016-01-01

Mass spectrometry-based metabolomics has become increasingly popular in molecular medicine. High-definition mass spectrometry (MS), coupled with pattern recognition methods, have been carried out to obtain comprehensive metabolite profiling and metabolic pathway of large biological datasets. This sets the scene for a new and powerful diagnostic approach. Analysis of the key metabolites in body fluids has become an important part of improving disease diagnosis. With technological advances in analytical techniques, the ability to measure low-molecular-weight metabolites in bio-samples provides a powerful platform for identifying metabolites that are uniquely correlated with a specific human disease. MS-based metabolomics can lead to enhanced understanding of disease mechanisms and to new diagnostic markers and has a strong potential to contribute to improving early diagnosis of diseases. This review will highlight the importance and benefit with certain characteristic examples of MS-metabolomics for identifying metabolic pathways and metabolites that accurately screen for potential diagnostic biomarkers of diseases. Copyright © 2015 John Wiley & Sons, Ltd.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

PubMed Central

Ramalingam, Abirami; Kudapa, Himabindu; Pazhamala, Lekha T.; Weckwerth, Wolfram; Varshney, Rajeev K.

2015-01-01

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these “omics” approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs. PMID:26734026

A metabolomics guided exploration of marine natural product chemical space.

PubMed

Floros, Dimitrios J; Jensen, Paul R; Dorrestein, Pieter C; Koyama, Nobuhiro

2016-09-01

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections. Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs. In this work we utilize untargeted LC-MS/MS based metabolomics together with molecular networking to. This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B. Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

Stable isotope-resolved metabolomics and applications for drug development

PubMed Central

Fan, Teresa W-M.; Lorkiewicz, Pawel; Sellers, Katherine; Moseley, Hunter N.B.; Higashi, Richard M.; Lane, Andrew N.

2012-01-01

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality. PMID:22212615

Metabolomic approach for improving ethanol stress tolerance in Saccharomyces cerevisiae.

PubMed

Ohta, Erika; Nakayama, Yasumune; Mukai, Yukio; Bamba, Takeshi; Fukusaki, Eiichiro

2016-04-01

The budding yeast Saccharomyces cerevisiae is widely used for brewing and ethanol production. The ethanol sensitivity of yeast cells is still a serious problem during ethanol fermentation, and a variety of genetic approaches (e.g., random mutant screening under selective pressure of ethanol) have been developed to improve ethanol tolerance. In this study, we developed a strategy for improving ethanol tolerance of yeast cells based on metabolomics as a high-resolution quantitative phenotypic analysis. We performed gas chromatography-mass spectrometry analysis to identify and quantify 36 compounds on 14 mutant strains including knockout strains for transcription factor and metabolic enzyme genes. A strong relation between metabolome of these mutants and their ethanol tolerance was observed. Data mining of the metabolomic analysis showed that several compounds (such as trehalose, valine, inositol and proline) contributed highly to ethanol tolerance. Our approach successfully detected well-known ethanol stress related metabolites such as trehalose and proline thus, to further prove our strategy, we focused on valine and inositol as the most promising target metabolites in our study. Our results show that simultaneous deletion of LEU4 and LEU9 (leading to accumulation of valine) or INM1 and INM2 (leading to reduction of inositol) significantly enhanced ethanol tolerance. This study shows the potential of the metabolomic approach to identify target genes for strain improvement of S. cerevisiae with higher ethanol tolerance. Copyright © 2015 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

PubMed

Wei, Runmin; Wang, Jingye; Su, Mingming; Jia, Erik; Chen, Shaoqiu; Chen, Tianlu; Ni, Yan

2018-01-12

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).

Characterizing Dissolved Organic Matter and Metabolites in an Actively Serpentinizing Ophiolite Using Global Metabolomics Techniques

NASA Astrophysics Data System (ADS)

Seyler, L. M.; Rempfert, K. R.; Kraus, E. A.; Spear, J. R.; Templeton, A. S.; Schrenk, M. O.

2017-12-01

Environmental metabolomics is an emerging approach used to study ecosystem properties. Through bioinformatic comparisons to metagenomic data sets, metabolomics can be used to study microbial adaptations and responses to varying environmental conditions. Since the techniques are highly parallel to organic geochemistry approaches, metabolomics can also provide insight into biogeochemical processes. These analyses are a reflection of metabolic potential and intersection with other organisms and environmental components. Here, we used an untargeted metabolomics approach to characterize dissolved organic carbon and aqueous metabolites from groundwater obtained from an actively serpentinizing habitat. Serpentinites are known to support microbial communities that feed off of the products of serpentinization (such as methane and H2 gas), while adapted to harsh environmental conditions such as high pH and low DIC availability. However, the biochemistry of microbial populations that inhabit these environments are understudied and are complicated by overlapping biotic and abiotic processes. The aim of this study was to identify potential sources of carbon in an environment that is depleted of soluble inorganic carbon, and to characterize the flow of metabolites and describe overlapping biogenic and abiogenic processes impacting carbon cycling in serpentinizing rocks. We applied untargeted metabolomics techniques to groundwater taken from a series of wells drilled into the Semail Ophiolite in Oman.. Samples were analyzed via quadrupole time-of-flight liquid chromatography tandem mass spectrometry (QToF-LC/MS/MS). Metabolomes and metagenomic data were imported into Progenesis QI software for statistical analysis and correlation, and metabolic networks constructed using the Genome-Linked Application for Metabolic Maps (GLAMM), a web interface tool. Further multivariate statistical analyses and quality control was performed using EZinfo. Pools of dissolved organic carbon could readily be distinguished based on their source rock and the pH of the groundwater sample. Our results are promising regarding the future use of metabolomics techniques in this and other serpentinizing environments, for the identification of nutrients, biomarkers and metabolic pathways in the subsurface biosphere.

A novel approach to the simultaneous extraction and non-targeted analysis of the small molecules metabolome and lipidome using 96-well solid phase extraction plates with column-switching technology.

PubMed

Li, Yubo; Zhang, Zhenzhu; Liu, Xinyu; Li, Aizhu; Hou, Zhiguo; Wang, Yuming; Zhang, Yanjun

2015-08-28

This study combines solid phase extraction (SPE) using 96-well plates with column-switching technology to construct a rapid and high-throughput method for the simultaneous extraction and non-targeted analysis of small molecules metabolome and lipidome based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. This study first investigated the columns and analytical conditions for small molecules metabolome and lipidome, separated by an HSS T3 and BEH C18 columns, respectively. Next, the loading capacity and actuation duration of SPE were further optimized. Subsequently, SPE and column switching were used together to rapidly and comprehensively analyze the biological samples. The experimental results showed that the new analytical procedure had good precision and maintained sample stability (RSD<15%). The method was then satisfactorily applied to more widely analyze the small molecules metabolome and lipidome to test the throughput. The resulting method represents a new analytical approach for biological samples, and a highly useful tool for researches in metabolomics and lipidomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography.

PubMed

Lee, Jang-Eun; Lee, Bum-Jin; Chung, Jin-Oh; Kim, Hak-Nam; Kim, Eun-Hee; Jung, Sungheuk; Lee, Hyosang; Lee, Sang-Jun; Hong, Young-Shick

2015-05-01

Numerous factors such as geographical origin, cultivar, climate, cultural practices, and manufacturing processes influence the chemical compositions of tea, in the same way as growing conditions and grape variety affect wine quality. However, the relationships between these factors and tea chemical compositions are not well understood. In this study, a new approach for non-targeted or global analysis, i.e., metabolomics, which is highly reproducible and statistically effective in analysing a diverse range of compounds, was used to better understand the metabolome of Camellia sinensis and determine the influence of environmental factors, including geography, climate, and cultural practices, on tea-making. We found a strong correlation between environmental factors and the metabolome of green, white, and oolong teas from China, Japan, and South Korea. In particular, multivariate statistical analysis revealed strong inter-country and inter-city relationships in the levels of theanine and catechin derivatives found in green and white teas. This information might be useful for assessing tea quality or producing distinct tea products across different locations, and highlights simultaneous identification of diverse tea metabolites through an NMR-based metabolomics approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Conventional and Advanced Separations in Mass Spectrometry-Based Metabolomics: Methodologies and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heyman, Heino M.; Zhang, Xing; Tang, Keqi

2016-02-16

Metabolomics is the quantitative analysis of all metabolites in a given sample. Due to the chemical complexity of the metabolome, optimal separations are required for comprehensive identification and quantification of sample constituents. This chapter provides an overview of both conventional and advanced separations methods in practice for reducing the complexity of metabolite extracts delivered to the mass spectrometer detector, and covers gas chromatography (GC), liquid chromatography (LC), capillary electrophoresis (CE), supercritical fluid chromatography (SFC) and ion mobility spectrometry (IMS) separation techniques coupled with mass spectrometry (MS) as both uni-dimensional and as multi-dimensional approaches.

Comparative metabolomics reveals the mechanism of avermectin production enhancement by S-adenosylmethionine.

PubMed

Tian, Pingping; Cao, Peng; Hu, Dong; Wang, Depei; Zhang, Jian; Wang, Lin; Zhu, Yan; Gao, Qiang

2017-05-01

It was found that S-adenosylmethionine (SAM) could effectively improve avermectin titer with 30-60 μg/mL addition to FH medium. To clearly elucidate the mechanism of SAM on intracellular metabolites of Streptomyces avermitilis, a GC-MS-based comparative metabolomics approach was carried out. First, 230 intracellular metabolites were identified and 14 of them remarkably influenced avermectin biosynthesis were discriminative biomarkers between non-SAM groups and SAM-treated groups by principal components analysis (PCA) and partial least squares (PLS). Based on further key metabolic pathway analyses, these biomarkers, such as glucose, oxaloacetic acid, fatty acids (in soybean oil), threonine, valine, and leucine, were identified as potentially beneficial precursors and added in medium. Compared with single-precursor feeding, the combined feeding of the precursors and SAM markedly increased the avermectin titer. The co-feeding approach not only directly verified our hypothesis on the mechanism of SAM by comparative metabolomics, but also provided a novel strategy to increase avermectin production.

Comparative metabolomic and ionomic approach for abundant fishes in estuarine environments of Japan

PubMed Central

Yoshida, Seiji; Date, Yasuhiro; Akama, Makiko; Kikuchi, Jun

2014-01-01

Environmental metabolomics or ionomics is widely used to characterize the effects of environmental stressors on the health of aquatic organisms. However, most studies have focused on liver and muscle tissues of fish, and little is known about how the other organs are affected by environmental perturbations and effects such as metal pollutants or eutrophication. We examined the metabolic and mineral profiles of three kinds of abundant fishes in estuarine ecosystem, yellowfin goby, urohaze-goby, and juvenile Japanese seabass sampled from Tsurumi River estuary, Japan. Multivariate analyses, including nuclear magnetic resonance-based metabolomics and inductively coupled plasma optical emission spectrometry-based ionomics approaches, revealed that the profiles were clustered according to differences among body tissues rather than differences in body size, sex, and species. The metabolic and mineral profiles of the muscle and fin tissues, respectively, suggest that these tissues are most appropriate for evaluating environmental perturbations. Such analyses will be highly useful in evaluating the environmental variation and diversity in aquatic ecosystems. PMID:25387575

Metabolomics and Diabetes: Analytical and Computational Approaches

PubMed Central

Sas, Kelli M.; Karnovsky, Alla; Michailidis, George

2015-01-01

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field. PMID:25713200

Environmental metabolomics: a SWOT analysis (strengths, weaknesses, opportunities, and threats).

PubMed

Miller, Marion G

2007-02-01

Metabolomic approaches have the potential to make an exceptional contribution to understanding how chemicals and other environmental stressors can affect both human and environmental health. However, the application of metabolomics to environmental exposures, although getting underway, has not yet been extensively explored. This review will use a SWOT analysis model to discuss some of the strengths, weaknesses, opportunities, and threats that are apparent to an investigator venturing into this relatively new field. SWOT has been used extensively in business settings to uncover new outlooks and identify problems that would impede progress. The field of environmental metabolomics provides great opportunities for discovery, and this is recognized by a high level of interest in potential applications. However, understanding the biological consequence of environmental exposures can be confounded by inter- and intra-individual differences. Metabolomic profiles can yield a plethora of data, the interpretation of which is complex and still being evaluated and researched. The development of the field will depend on the availability of technologies for data handling and that permit ready access metabolomic databases. Understanding the relevance of metabolomic endpoints to organism health vs adaptation vs variation is an important step in understanding what constitutes a substantive environmental threat. Metabolomic applications in reproductive research are discussed. Overall, the development of a comprehensive mechanistic-based interpretation of metabolomic changes offers the possibility of providing information that will significantly contribute to the protection of human health and the environment.

A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism.

PubMed

Jacob, Minnie; Malkawi, Abeer; Albast, Nour; Al Bougha, Salam; Lopata, Andreas; Dasouki, Majed; Abdel Rahman, Anas M

2018-09-26

Metabolome, the ultimate functional product of the genome, can be studied through identification and quantification of small molecules. The global metabolome influences the individual phenotype through clinical and environmental interventions. Metabolomics has become an integral part of clinical research and allowed for another dimension of better understanding of disease pathophysiology and mechanism. More than 95% of the clinical biochemistry laboratory routine workload is based on small molecular identification, which can potentially be analyzed through metabolomics. However, multiple challenges in clinical metabolomics impact the entire workflow and data quality, thus the biological interpretation needs to be standardized for a reproducible outcome. Herein, we introduce the establishment of a comprehensive targeted metabolomics method for a panel of 220 clinically relevant metabolites using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) standardized for clinical research. The sensitivity, reproducibility and molecular stability of each targeted metabolite (amino acids, organic acids, acylcarnitines, sugars, bile acids, neurotransmitters, polyamines, and hormones) were assessed under multiple experimental conditions. The metabolic tissue distribution was determined in various rat organs. Furthermore, the method was validated in dry blood spot (DBS) samples collected from patients known to have various inborn errors of metabolism (IEMs). Using this approach, our panel appears to be sensitive and robust as it demonstrated differential and unique metabolic profiles in various rat tissues. Also, as a prospective screening method, this panel of diverse metabolites has the ability to identify patients with a wide range of IEMs who otherwise may need multiple, time-consuming and expensive biochemical assays causing a delay in clinical management. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data.

PubMed

Thonusin, Chanisa; IglayReger, Heidi B; Soni, Tanu; Rothberg, Amy E; Burant, Charles F; Evans, Charles R

2017-11-10

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography - mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Sweat: a sample with limited present applications and promising future in metabolomics.

PubMed

Mena-Bravo, A; Luque de Castro, M D

2014-03-01

Sweat is a biofluid with present scant use as clinical sample. This review tries to demonstrate the advantages of sweat over other biofluids such as blood or urine for routine clinical analyses and the potential when related to metabolomics. With this aim, critical discussion of sweat samplers and equipment for analysis of target compounds in this sample is made. Well established routine analyses in sweat as is that to diagnose cystic fibrosis, and the advantages and disadvantages of sweat versus urine or blood for doping control have also been discussed. Methods for analytes such as essential metals and xenometals, ethanol and electrolytes in sweat in fact constitute target metabolomics approaches or belong to any metabolomics subdiscipline such as metallomics, ionomics or xenometabolomics. The higher development of biomarkers based on genomics or proteomics as omics older than metabolomics is discussed and also the potential role of metabolomics in systems biology taking into account its emergent implementation. Normalization of the volume of sampled sweat constitutes a present unsolved shortcoming that deserves investigation. Foreseeable trends in this area are outlined. Copyright © 2013 Elsevier B.V. All rights reserved.

Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data.

PubMed

Goodwin, Cody R; Sherrod, Stacy D; Marasco, Christina C; Bachmann, Brian O; Schramm-Sapyta, Nicole; Wikswo, John P; McLean, John A

2014-07-01

A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.

Gas Chromatography- Mass Spectrometry Based Metabolomic Approach for Optimization and Toxicity Evaluation of Earthworm Sub-Lethal Responses to Carbofuran

PubMed Central

Saxena, Prem Narain

2013-01-01

Despite recent advances in understanding mechanism of toxicity, the development of biomarkers (biochemicals that vary significantly with exposure to chemicals) for pesticides and environmental contaminants exposure is still a challenging task. Carbofuran is one of the most commonly used pesticides in agriculture and said to be most toxic carbamate pesticide. It is necessary to identify the biochemicals that can vary significantly after carbofuran exposure on earthworms which will help to assess the soil ecotoxicity. Initially, we have optimized the extraction conditions which are suitable for high-throughput gas chromatography mass spectrometry (GC-MS) based metabolomics for the tissue of earthworm, Metaphire posthuma. Upon evaluation of five different extraction solvent systems, 80% methanol was found to have good extraction efficiency based on the yields of metabolites, multivariate analysis, total number of peaks and reproducibility of metabolites. Later the toxicity evaluation was performed to characterize the tissue specific metabolomic perturbation of earthworm, Metaphire posthuma after exposure to carbofuran at three different concentration levels (0.15, 0.3 and 0.6 mg/kg of soil). Seventeen metabolites, contributing to the best classification performance of highest dose dependent carbofuran exposed earthworms from healthy controls were identified. This study suggests that GC-MS based metabolomic approach was precise and sensitive to measure the earthworm responses to carbofuran exposure in soil, and can be used as a promising tool for environmental eco-toxicological studies. PMID:24324663

LC-MS based metabolomics and chemometrics study of the toxic effects of copper on Saccharomyces cerevisiae.

PubMed

Farrés, Mireia; Piña, Benjamí; Tauler, Romà

2016-08-01

Copper containing fungicides are used to protect vineyards from fungal infections. Higher residues of copper in grapes at toxic concentrations are potentially toxic and affect the microorganisms living in vineyards, such as Saccharomyces cerevisiae. In this study, the response of the metabolic profiles of S. cerevisiae at different concentrations of copper sulphate (control, 1 mM, 3 mM and 6 mM) was analysed by liquid chromatography coupled to mass spectrometry (LC-MS) and multivariate curve resolution-alternating least squares (MCR-ALS) using an untargeted metabolomics approach. Peak areas of the MCR-ALS resolved elution profiles in control and in Cu(ii)-treated samples were compared using partial least squares regression (PLSR) and PLS-discriminant analysis (PLS-DA), and the intracellular metabolites best contributing to sample discrimination were selected and identified. Fourteen metabolites showed significant concentration changes upon Cu(ii) exposure, following a dose-response effect. The observed changes were consistent with the expected effects of Cu(ii) toxicity, including oxidative stress and DNA damage. This research confirmed that LC-MS based metabolomics coupled to chemometric methods are a powerful approach for discerning metabolomics changes in S. cerevisiae and for elucidating modes of toxicity of environmental stressors, including heavy metals like Cu(ii).

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

PubMed

Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

2015-02-01

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data. Copyright © 2014 Elsevier Inc. All rights reserved.

Metabolomic Studies in Drosophila.

PubMed

Cox, James E; Thummel, Carl S; Tennessen, Jason M

2017-07-01

Metabolomic analysis provides a powerful new tool for studies of Drosophila physiology. This approach allows investigators to detect thousands of chemical compounds in a single sample, representing the combined contributions of gene expression, enzyme activity, and environmental context. Metabolomics has been used for a wide range of studies in Drosophila , often providing new insights into gene function and metabolic state that could not be obtained using any other approach. In this review, we survey the uses of metabolomic analysis since its entry into the field. We also cover the major methods used for metabolomic studies in Drosophila and highlight new directions for future research. Copyright © 2017 by the Genetics Society of America.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality.

PubMed

Altmaier, Elisabeth; Emeny, Rebecca T; Krumsiek, Jan; Lacruz, Maria E; Lukaschek, Karoline; Häfner, Sibylle; Kastenmüller, Gabi; Römisch-Margl, Werner; Prehn, Cornelia; Mohney, Robert P; Evans, Anne M; Milburn, Michael V; Illig, Thomas; Adamski, Jerzy; Theis, Fabian; Suhre, Karsten; Ladwig, Karl-Heinz

2013-08-01

Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality. Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study. 668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality. This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease. Copyright © 2012 Elsevier Ltd. All rights reserved.

Development of a metabolomic approach based on liquid chromatography-high resolution mass spectrometry to screen for clenbuterol abuse in calves.

PubMed

Courant, Frédérique; Pinel, Gaud; Bichon, Emmanuelle; Monteau, Fabrice; Antignac, Jean-Philippe; Le Bizec, Bruno

2009-08-01

Beta-agonist compounds can be misused in food-producing animals for growth promoting purposes. Efficient methods based on mass spectrometry detection have been developed to ensure the control of such veterinary drug residues. Nevertheless, the use of "cocktails" composed of mixtures of low amounts of several substances as well as the synthesis of new compounds of unknown structure prevent efficient prevention. To circumvent those problems, new analytical tools able to detect such abuse are today mandatory. In this context, metabolomics may represent a new emerging strategy for investigating the global physiological effects associated to a family of substances and therefore, to suspect the administration of beta-agonists (either "cocktails" or unknown compounds). As a first demonstration of feasibility, an untargeted metabolomic approach based on liquid chromatography coupled to high resolution mass spectrometry measurements was developed and made it possible to highlight metabolic modifications in urine consecutively to a clenbuterol administration. By the means of chemometrics, those metabolic differences were used to build predictive models able to suspect clenbuterol administration in calves. This new approach may be considered of valuable interest to overcome current limitations in the control of growth promoters' abuse, with promising perspectives in terms of screening.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

PubMed Central

Puchades-Carrasco, Leonor; Pineda-Lucena, Antonio

2017-01-01

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area. PMID:28685691

Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites.

PubMed

Mahieu, Nathaniel G; Patti, Gary J

2017-10-03

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13 C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets.

PubMed

Carroll, Adam J; Badger, Murray R; Harvey Millar, A

2010-07-14

Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis. Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline. MetabolomeExpress https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.

1H NMR-metabolomics: can they be a useful tool in our understanding of cardiac arrest?

PubMed

Chalkias, Athanasios; Fanos, Vassilios; Noto, Antonio; Castrén, Maaret; Gulati, Anil; Svavarsdóttir, Hildigunnur; Iacovidou, Nicoletta; Xanthos, Theodoros

2014-05-01

This review focuses on the presentation of the emerging technology of metabolomics, a promising tool for the detection of identifying the unrevealed biological pathways that lead to cardiac arrest. The electronic bases of PubMed, Scopus, and EMBASE were searched. Research terms were identified using the MESH database and were combined thereafter. Initial search terms were "cardiac arrest", "cardiopulmonary resuscitation", "post-cardiac arrest syndrome" combined with "metabolomics". Metabolomics allow the monitoring of hundreds of metabolites from tissues or body fluids and already influence research in the field of cardiac metabolism. This approach has elucidated several pathophysiological mechanisms and identified profiles of metabolic changes that can be used to follow the disease processes occurring in the peri-arrest period. This can be achieved through leveraging the strengths of unbiased metabolome-wide scans, which include thousands of final downstream products of gene transcription, enzyme activity and metabolic products of extraneously administered substances, in order to identify a metabolomic fingerprint associated with an increased risk of cardiac arrest. Although this technology is still under development, metabolomics is a promising tool for elucidating biological pathways and discovering clinical biomarkers, strengthening the efforts for optimizing both the prevention and treatment of cardiac arrest. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Metabolomics in chemical ecology.

PubMed

Kuhlisch, Constanze; Pohnert, Georg

2015-07-01

Chemical ecology elucidates the nature and role of natural products as mediators of organismal interactions. The emerging techniques that can be summarized under the concept of metabolomics provide new opportunities to study such environmentally relevant signaling molecules. Especially comparative tools in metabolomics enable the identification of compounds that are regulated during interaction situations and that might play a role as e.g. pheromones, allelochemicals or in induced and activated defenses. This approach helps overcoming limitations of traditional bioassay-guided structure elucidation approaches. But the power of metabolomics is not limited to the comparison of metabolic profiles of interacting partners. Especially the link to other -omics techniques helps to unravel not only the compounds in question but the entire biosynthetic and genetic re-wiring, required for an ecological response. This review comprehensively highlights successful applications of metabolomics in chemical ecology and discusses existing limitations of these novel techniques. It focuses on recent developments in comparative metabolomics and discusses the use of metabolomics in the systems biology of organismal interactions. It also outlines the potential of large metabolomics initiatives for model organisms in the field of chemical ecology.

Development of an Integrated Metabolomic Profiling Approach for Infectious Diseases Research

PubMed Central

Lv, Haitao; Hung, Chia S.; Chaturvedi, Kaveri S.; Hooton, Thomas M.; Henderson, Jeffrey P.

2013-01-01

Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ∼2300 molecular features. Principal components analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts. PMID:21922104

Metabolomics through the lens of precision cardiovascular medicine.

PubMed

Lam, Sin Man; Wang, Yuan; Li, Bowen; Du, Jie; Shui, Guanghou

2017-03-20

Metabolomics, which targets at the extensive characterization and quantitation of global metabolites from both endogenous and exogenous sources, has emerged as a novel technological avenue to advance the field of precision medicine principally driven by genomics-oriented approaches. In particular, metabolomics has revealed the cardinal roles that the environment exerts in driving the progression of major diseases threatening public health. Herein, the existent and potential applications of metabolomics in two key areas of precision cardiovascular medicine will be critically discussed: 1) the use of metabolomics in unveiling novel disease biomarkers and pathological pathways; 2) the contribution of metabolomics in cardiovascular drug development. Major issues concerning the statistical handling of big data generated by metabolomics, as well as its interpretation, will be briefly addressed. Finally, the need for integration of various omics branches and adopting a multi-omics approach to precision medicine will be discussed. Copyright © 2017 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.

Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach.

PubMed

Sales, C; Cervera, M I; Gil, R; Portolés, T; Pitarch, E; Beltran, J

2017-02-01

The novel atmospheric pressure chemical ionization (APCI) source has been used in combination with gas chromatography (GC) coupled to hybrid quadrupole time-of-flight (QTOF) mass spectrometry (MS) for determination of volatile components of olive oil, enhancing its potential for classification of olive oil samples according to their quality using a metabolomics-based approach. The full-spectrum acquisition has allowed the detection of volatile organic compounds (VOCs) in olive oil samples, including Extra Virgin, Virgin and Lampante qualities. A dynamic headspace extraction with cartridge solvent elution was applied. The metabolomics strategy consisted of three different steps: a full mass spectral alignment of GC-MS data using MzMine 2.0, a multivariate analysis using Ez-Info and the creation of the statistical model with combinations of responses for molecular fragments. The model was finally validated using blind samples, obtaining an accuracy in oil classification of 70%, taking the official established method, "PANEL TEST", as reference. Copyright © 2016 Elsevier Ltd. All rights reserved.

A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression.

PubMed

Blasco, Hélène; Patin, Franck; Descat, Amandine; Garçon, Guillaume; Corcia, Philippe; Gelé, Patrick; Lenglet, Timothée; Bede, Peter; Meininger, Vincent; Devos, David; Gossens, Jean François; Pradat, Pierre-François

2018-01-01

There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10-6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

PubMed

Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang

2018-02-01

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolomics Approach for Toxicity Screening of Volatile Substances

EPA Science Inventory

In 2007 the National Research Council envisioned the need for inexpensive, high throughput, cell based toxicity testing methods relevant to human health. High Throughput Screening (HTS) in vitro screening approaches have addressed these problems by using robotics. However, the ch...

Time-saving design of experiment protocol for optimization of LC-MS data processing in metabolomic approaches.

PubMed

Zheng, Hong; Clausen, Morten Rahr; Dalsgaard, Trine Kastrup; Mortensen, Grith; Bertram, Hanne Christine

2013-08-06

We describe a time-saving protocol for the processing of LC-MS-based metabolomics data by optimizing parameter settings in XCMS and threshold settings for removing noisy and low-intensity peaks using design of experiment (DoE) approaches including Plackett-Burman design (PBD) for screening and central composite design (CCD) for optimization. A reliability index, which is based on evaluation of the linear response to a dilution series, was used as a parameter for the assessment of data quality. After identifying the significant parameters in the XCMS software by PBD, CCD was applied to determine their values by maximizing the reliability and group indexes. Optimal settings by DoE resulted in improvements of 19.4% and 54.7% in the reliability index for a standard mixture and human urine, respectively, as compared with the default setting, and a total of 38 h was required to complete the optimization. Moreover, threshold settings were optimized by using CCD for further improvement. The approach combining optimal parameter setting and the threshold method improved the reliability index about 9.5 times for a standards mixture and 14.5 times for human urine data, which required a total of 41 h. Validation results also showed improvements in the reliability index of about 5-7 times even for urine samples from different subjects. It is concluded that the proposed methodology can be used as a time-saving approach for improving the processing of LC-MS-based metabolomics data.

Metabolomic approach for discrimination of processed ginseng genus (Panax ginseng and Panax quinquefolius) using UPLC-QTOF MS

PubMed Central

Park, Hee-Won; In, Gyo; Kim, Jeong-Han; Cho, Byung-Goo; Han, Gyeong-Ho; Chang, Il-Moo

2013-01-01

Discriminating between two herbal medicines (Panax ginseng and Panax quinquefolius), with similar chemical and physical properties but different therapeutic effects, is a very serious and difficult problem. Differentiation between two processed ginseng genera is even more difficult because the characteristics of their appearance are very similar. An ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS)-based metabolomic technique was applied for the metabolite profiling of 40 processed P. ginseng and processed P. quinquefolius. Currently known biomarkers such as ginsenoside Rf and F11 have been used for the analysis using the UPLC-photodiode array detector. However, this method was not able to fully discriminate between the two processed ginseng genera. Thus, an optimized UPLC-QTOF-based metabolic profiling method was adapted for the analysis and evaluation of two processed ginseng genera. As a result, all known biomarkers were identified by the proposed metabolomics, and additional potential biomarkers were extracted from the huge amounts of global analysis data. Therefore, it is expected that such metabolomics techniques would be widely applied to the ginseng research field. PMID:24558312

Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

PubMed

Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

2018-04-01

Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

Rifaximin Modulates the Vaginal Microbiome and Metabolome in Women Affected by Bacterial Vaginosis

PubMed Central

Picone, Gianfranco; Cruciani, Federica; Brigidi, Patrizia; Calanni, Fiorella; Donders, Gilbert; Capozzi, Francesco; Vitali, Beatrice

2014-01-01

Bacterial vaginosis (BV) is a common vaginal disorder characterized by the decrease of lactobacilli and overgrowth of Gardnerella vaginalis and resident anaerobic vaginal bacteria. In the present work, the effects of rifaximin vaginal tablets on vaginal microbiota and metabolome of women affected by BV were investigated by combining quantitative PCR and a metabolomic approach based on 1H nuclear magnetic resonance. To highlight the general trends of the bacterial communities and metabolomic profiles in response to the antibiotic/placebo therapy, a multivariate statistical strategy was set up based on the trajectories traced by vaginal samples in a principal component analysis space. Our data demonstrated the efficacy of rifaximin in restoring a health-like condition in terms of both bacterial communities and metabolomic features. In particular, rifaximin treatment was significantly associated with an increase in the lactobacillus/BV-related bacteria ratio, as well as with an increase in lactic acid concentration and a decrease of a pool of metabolites typically produced by BV-related bacteria (acetic acid, succinate, short-chain fatty acids, and biogenic amines). Among the tested dosages of rifaximin (100 and 25 mg for 5 days and 100 mg for 2 days), 25 mg for 5 days was found to be the most effective. PMID:24709255

Metabolomics-Driven Nutraceutical Evaluation of Diverse Green Tea Cultivars

PubMed Central

Ida, Megumi; Kosaka, Reia; Miura, Daisuke; Wariishi, Hiroyuki; Maeda-Yamamoto, Mari; Nesumi, Atsushi; Saito, Takeshi; Kanda, Tomomasa; Yamada, Koji; Tachibana, Hirofumi

2011-01-01

Background Green tea has various health promotion effects. Although there are numerous tea cultivars, little is known about the differences in their nutraceutical properties. Metabolic profiling techniques can provide information on the relationship between the metabolome and factors such as phenotype or quality. Here, we performed metabolomic analyses to explore the relationship between the metabolome and health-promoting attributes (bioactivity) of diverse Japanese green tea cultivars. Methodology/Principal Findings We investigated the ability of leaf extracts from 43 Japanese green tea cultivars to inhibit thrombin-induced phosphorylation of myosin regulatory light chain (MRLC) in human umbilical vein endothelial cells (HUVECs). This thrombin-induced phosphorylation is a potential hallmark of vascular endothelial dysfunction. Among the tested cultivars, Cha Chuukanbohon Nou-6 (Nou-6) and Sunrouge (SR) strongly inhibited MRLC phosphorylation. To evaluate the bioactivity of green tea cultivars using a metabolomics approach, the metabolite profiles of all tea extracts were determined by high-performance liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analyses, principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA), revealed differences among green tea cultivars with respect to their ability to inhibit MRLC phosphorylation. In the SR cultivar, polyphenols were associated with its unique metabolic profile and its bioactivity. In addition, using partial least-squares (PLS) regression analysis, we succeeded in constructing a reliable bioactivity-prediction model to predict the inhibitory effect of tea cultivars based on their metabolome. This model was based on certain identified metabolites that were associated with bioactivity. When added to an extract from the non-bioactive cultivar Yabukita, several metabolites enriched in SR were able to transform the extract into a bioactive extract. Conclusions/Significance Our findings suggest that metabolic profiling is a useful approach for nutraceutical evaluation of the health promotion effects of diverse tea cultivars. This may propose a novel strategy for functional food design. PMID:21853132

Toxic responses of Perna viridis hepatopancreas exposed to DDT, benzo(a)pyrene and their mixture uncovered by iTRAQ-based proteomics and NMR-based metabolomics.

PubMed

Song, Qinqin; Zhou, Hailong; Han, Qian; Diao, Xiaoping

2017-11-01

Dichlorodiphenyltrichloroethane (DDT) and benzo(a)pyrene (BaP) are environmental estrogens (EEs) that are ubiquitous in the marine environment. In the present study, we integrated isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic and nuclear magnetic resonance (NMR)-based metabolomic approaches to explore the toxic responses of green mussel hepatopancreas exposed to DDT (10μg/L), BaP (10μg/L) and their mixture. The metabolic responses indicated that BaP primarily disturbed energy metabolism and osmotic regulation in the hepatopancreas of the male green mussel P. viridis. Both DDT and the mixture of DDT and BaP perturbed the energy metabolism and osmotic regulation in P. viridis. The proteomic responses revealed that BaP affected the proteins involved in energy metabolism, material transformation, cytoskeleton, stress responses, reproduction and development in green mussels. DDT exposure could change the proteins involved in primary metabolism, stress responses, cytoskeleton and signal transduction. However, the mixture of DDT and BaP altered proteins associated with material and energy metabolism, stress responses, signal transduction, reproduction and development, cytoskeleton and apoptosis. This study showed that iTRAQ-based proteomic and NMR-based metabolomic approaches could effectively elucidate the essential molecular mechanism of disturbances in hepatopancreas function of green mussels exposed to environmental estrogens. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia

PubMed Central

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100–300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making. PMID:29095932

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia.

PubMed

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Sinha, Neeraj; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100-300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making.

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data. PMID:16788595

Non-targeted metabolomic approach reveals urinary metabolites linked to steroid biosynthesis pathway after ingestion of citrus juice.

PubMed

Medina, S; Ferreres, F; García-Viguera, C; Horcajada, M N; Orduna, J; Savirón, M; Zurek, G; Martínez-Sanz, J M; Gil, J I; Gil-Izquierdo, A

2013-01-15

Citrus juice intake has been highlighted because of its health-promoting effects. LC-MS based metabolomics approaches are applied to obtain a better knowledge on changes in the concentration of metabolites due to its dietary intake and allow a better understanding of involved metabolic pathways. Eight volunteers daily consumed 400 mL of juice for four consecutive days and urine samples were collected before intake and 24h after each citrus juice intake. Urine samples were analysed by nanoHPLC-q-TOF, followed by principal component analysis (PCA) and Student's t-test (p<0.05). PCA showed a separation between two groups (before and after citrus juice consumption). This approach allowed the identification of four endocrine compounds (tetrahydroaldosterone-3-glucuronide, cortolone-3-glucuronide, testosterone-glucuronide and 17-hydroxyprogesterone), which belonged to the steroid biosynthesis pathway as significant metabolites upregulated by citrus juice intake. Additionally, these results confirmed the importance of using the non-targeted metabolomics technique to identify new endogenous metabolites, up- or down-regulated as a consequence of food intake. Copyright © 2012 Elsevier Ltd. All rights reserved.

Metabolomic approach to optimizing and evaluating antibiotic treatment in the axenic culture of cyanobacterium Nostoc flagelliforme.

PubMed

Han, Pei-pei; Jia, Shi-ru; Sun, Ying; Tan, Zhi-lei; Zhong, Cheng; Dai, Yu-jie; Tan, Ning; Shen, Shi-gang

2014-09-01

The application of antibiotic treatment with assistance of metabolomic approach in axenic isolation of cyanobacterium Nostoc flagelliforme was investigated. Seven antibiotics were tested at 1-100 mg L(-1), and order of tolerance of N. flagelliforme cells was obtained as kanamycin > ampicillin, tetracycline > chloromycetin, gentamicin > spectinomycin > streptomycin. Four antibiotics were selected based on differences in antibiotic sensitivity of N. flagelliforme and associated bacteria, and their effects on N. flagelliforme cells including the changes of metabolic activity with antibiotics and the metabolic recovery after removal were assessed by a metabolomic approach based on gas chromatography-mass spectrometry combined with multivariate analysis. The results showed that antibiotic treatment had affected cell metabolism as antibiotics treated cells were metabolically distinct from control cells, but the metabolic activity would be recovered via eliminating antibiotics and the sequence of metabolic recovery time needed was spectinomycin, gentamicin > ampicillin > kanamycin. The procedures of antibiotic treatment have been accordingly optimized as a consecutive treatment starting with spectinomycin, then gentamicin, ampicillin and lastly kanamycin, and proved to be highly effective in eliminating the bacteria as examined by agar plating method and light microscope examination. Our work presented a strategy to obtain axenic culture of N. flagelliforme and provided a method for evaluating and optimizing cyanobacteria purification process through diagnosing target species cellular state.

Application of metabolomics to toxicology of drugs of abuse: A mini review of metabolomics approach to acute and chronic toxicity studies.

PubMed

Zaitsu, Kei; Hayashi, Yumi; Kusano, Maiko; Tsuchihashi, Hitoshi; Ishii, Akira

2016-02-01

Metabolomics has been widely applied to toxicological fields, especially to elucidate the mechanism of action of toxicity. In this review, metabolomics application with focus on the studies of chronic and acute toxicities of drugs of abuse like stimulants, opioids and the recently-distributed designer drugs will be presented in addition to an outline of basic analytical techniques used in metabolomics. Limitation of metabolomics studies and future perspectives will be also provided. Copyright © 2015 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Distinct signatures of host–microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet

PubMed Central

Walker, Alesia; Pfitzner, Barbara; Neschen, Susanne; Kahle, Melanie; Harir, Mourad; Lucio, Marianna; Moritz, Franco; Tziotis, Dimitrios; Witting, Michael; Rothballer, Michael; Engel, Marion; Schmid, Michael; Endesfelder, David; Klingenspor, Martin; Rattei, Thomas; Castell, Wolfgang zu; de Angelis, Martin Hrabé; Hartmann, Anton; Schmitt-Kopplin, Philippe

2014-01-01

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility. Gut microbial and liver metabolomics were followed by a combinatory approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and ultra performance liquid chromatography time of tlight MS/MS with subsequent multivariate statistical analysis, revealing distinctive host and microbial metabolome patterns between the C57J and the C57N strain. Many taurine-conjugated bile acids (TBAs) were significantly elevated in the cecum and decreased in liver samples from the C57J phenotype likely displaying different energy utilization behavior by the bacterial community and the host. Furthermore, several metabolite groups could specifically be associated with the C57N phenotype involving fatty acids, eicosanoids and urobilinoids. The mass differences based metabolite network approach enabled to extend the range of known metabolites to important bile acids (BAs) and novel taurine conjugates specific for both strains. In summary, our study showed clear alterations of the metabolome in the gastrointestinal tract and liver within a HFD-induced obesity mouse model in relation to the host–microbial nutritional adaptation. PMID:24906017

Metabolic profiling of goldfish (Carassius auratis) after long-term glyphosate-based herbicide exposure.

PubMed

Li, Ming-Hui; Ruan, Ling-Yu; Zhou, Jin-Wei; Fu, Yong-Hong; Jiang, Lei; Zhao, He; Wang, Jun-Song

2017-07-01

Glyphosate is an efficient herbicide widely used worldwide. However, its toxicity to non-targeted organisms has not been fully elucidated. In this study, the toxicity of glyphosate-based herbicide was evaluated on goldfish (Carassius auratus) after long-term exposure. Tissues of brains, kidneys and livers were collected and submitted to NMR-based metabolomics analysis and histopathological inspection. Plasma was collected and the blood biochemical indexes of AST, ALT, BUN, CRE, LDH, SOD, GSH-Px, GR and MDA were measured. Long-term glyphosate exposure caused disorders of blood biochemical indexes and renal tissue injury in goldfish. Metabolomics analysis combined with correlation network analysis uncovered significant perturbations in oxidative stress, energy metabolism, amino acids metabolism and nucleosides metabolism in glyphosate dosed fish, which provide new clues to the toxicity of glyphosate. This integrated metabolomics approach showed its applicability in discovering the toxic mechanisms of pesticides, which provided new strategy for the assessment of the environmental risk of herbicides to non-target organisms. Copyright © 2017 Elsevier B.V. All rights reserved.

Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics.

PubMed

Niedenführ, Sebastian; ten Pierick, Angela; van Dam, Patricia T N; Suarez-Mendez, Camilo A; Nöh, Katharina; Wahl, S Aljoscha

2016-05-01

Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation. © 2015 Wiley Periodicals, Inc.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery.

PubMed

Khamis, Mona M; Adamko, Darryl J; El-Aneed, Anas

2017-03-01

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:115-134, 2017. © 2015 Wiley Periodicals, Inc.

CE-MS for metabolomics: developments and applications in the period 2012-2014.

PubMed

Ramautar, Rawi; Somsen, Govert W; de Jong, Gerhardus J

2015-01-01

In the field of metabolomics, CE-MS is now regarded as a useful complementary analytical technique for the profiling of (highly) polar ionogenic metabolites in biological samples. Over the past few years, significant advancements have been made in CE-MS approaches for metabolic profiling studies. This paper, which is a follow-up of three previous review papers covering the years 2000-2012 [Electrophoresis 2009, 30, 276-291; Electrophoresis 2011, 32, 52-65; Electrophoresis 2013, 34, 86-98], provides an update of these developments covering the scientific literature from July 2012 to June 2014. Attention will be paid to novel interfacing techniques for coupling CE to MS and their implications for metabolomics studies. The potential of CEC-MS and MEKC-MS are also considered, and CE-MS systems for high-throughput metabolic profiling are discussed. The applicability of CE-MS for metabolomics studies is demonstrated by representative examples in the fields of biomedical, clinical, microbial, plant, environmental, and food metabolomics. An overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, general conclusions and perspectives are given. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Innovative Approach for The Integration of Proteomics and Metabolomics Data In Severe Septic Shock Patients Stratified for Mortality.

PubMed

Cambiaghi, Alice; Díaz, Ramón; Martinez, Julia Bauzá; Odena, Antonia; Brunelli, Laura; Caironi, Pietro; Masson, Serge; Baselli, Giuseppe; Ristagno, Giuseppe; Gattinoni, Luciano; de Oliveira, Eliandre; Pastorelli, Roberta; Ferrario, Manuela

2018-04-27

In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicenter ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolites concentration and relative proteins abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three different elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and Partial least squares Discriminant Analysis were also implemented. All the obtained models correctly classified the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information.

Concordance of Changes in Metabolic Pathways Based on Plasma Metabolomics and Skeletal Muscle Transcriptomics in Type 1 Diabetes

PubMed Central

Dutta, Tumpa; Chai, High Seng; Ward, Lawrence E.; Ghosh, Aditya; Persson, Xuan-Mai T.; Ford, G. Charles; Kudva, Yogish C.; Sun, Zhifu; Asmann, Yan W.; Kocher, Jean-Pierre A.; Nair, K. Sreekumaran

2012-01-01

Insulin regulates many cellular processes, but the full impact of insulin deficiency on cellular functions remains to be defined. Applying a mass spectrometry–based nontargeted metabolomics approach, we report here alterations of 330 plasma metabolites representing 33 metabolic pathways during an 8-h insulin deprivation in type 1 diabetic individuals. These pathways included those known to be affected by insulin such as glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses and those hitherto unknown to be altered including prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and anti-inflammatory responses. A significant concordance of metabolome and skeletal muscle transcriptome–based pathways supports an assumption that plasma metabolites are chemical fingerprints of cellular events. Although insulin treatment normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes. Confirmation of many known pathways altered by insulin using a single blood test offers confidence in the current approach. Future research needs to be focused on newly discovered pathways affected by insulin deficiency and systemic insulin treatment to determine whether they contribute to the high morbidity and mortality in T1D despite insulin treatment. PMID:22415876

A Multi-Omic Systems-Based Approach Reveals Metabolic Markers of Bacterial Vaginosis and Insight into the Disease

PubMed Central

Yeoman, Carl J.; Thomas, Susan M.; Miller, Margret E. Berg; Ulanov, Alexander V.; Torralba, Manolito; Lucas, Sarah; Gillis, Marcus; Cregger, Melissa; Gomez, Andres; Ho, Mengfei; Leigh, Steven R.; Stumpf, Rebecca; Creedon, Douglas J.; Smith, Michael A.; Weisbaum, Jon S.; Nelson, Karen E.; Wilson, Brenda A.; White, Bryan A.

2013-01-01

Background Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms. Principal Findings 16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp. Conclusions The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV. PMID:23405259

A multi-omic systems-based approach reveals metabolic markers of bacterial vaginosis and insight into the disease.

PubMed

Yeoman, Carl J; Thomas, Susan M; Miller, Margret E Berg; Ulanov, Alexander V; Torralba, Manolito; Lucas, Sarah; Gillis, Marcus; Cregger, Melissa; Gomez, Andres; Ho, Mengfei; Leigh, Steven R; Stumpf, Rebecca; Creedon, Douglas J; Smith, Michael A; Weisbaum, Jon S; Nelson, Karen E; Wilson, Brenda A; White, Bryan A

2013-01-01

Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms. 16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp. The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV.

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

PubMed Central

Merino, Jordi; Leong, Aaron; Meigs, James B.

2018-01-01

Purpose of Review The purpose of this review was to summarize and reflect on advances over the past decade in human genetic and metabolomic discovery with particular focus on their contributions to type 2 diabetes (T2D) risk prediction. Recent Findings In the past 10 years, a combination of advances in genotyping efficiency, metabolomic profiling, bio-informatics approaches, and international collaboration have moved T2D genetics and metabolomics from a state of frustration to an abundance of new knowledge. Summary Efforts to control and prevent T2D have failed to stop this global epidemic. New approaches are needed, and although neither genetic nor metabolomic profiling yet have a clear clinical role, the rapid pace of accumulating knowledge offers the possibility for “multi-omic” prediction to improve health. PMID:29103096

1H NMR-based metabolomics investigation on the effects of petrochemical contamination in posterior adductor muscles of caged mussel Mytilus galloprovincialis.

PubMed

Cappello, Tiziana; Maisano, Maria; Mauceri, Angela; Fasulo, Salvatore

2017-08-01

Environmental metabolomics is a high-throughout approach that provides a snapshot of the metabolic status of an organism. In order to elucidate the biological effects of petrochemical contamination on aquatic invertebrates, mussels Mytilus galloprovincialis were caged at the "Augusta-Melilli-Priolo" petrochemical area and Brucoli (Sicily, south Italy), chosen as the reference site. After confirming the elevated concentrations of polycyclic aromatic hydrocarbons (PAHs) and mercury (Hg) in Augusta sediments in our previous work (Maisano et al., 2016a), herein an environmental metabolomics approach based on protonic nuclear magnetic resonance ( 1 H NMR), coupled with chemometrics, was applied on the mussel posterior adductor muscle (PAM), the main muscular system in bivalve molluscs. Amino acids, osmolytes, energy storage compounds, tricarboxylic acid cycle intermediates, and nucleotides, were found in PAM NMR spectra. Principal Component Analysis (PCA) indicated that mussels caged at the polluted site clustered separately from mussels from the control area, suggesting a clear differentiation between their metabolic profiles. Specifically, disorders in energy metabolism, alterations in amino acids metabolism, and disturbance in the osmoregulatory processes were observed in mussel PAM. Overall, findings from this work demonstrated the usefulness of applying an active biomonitoring strategy for environmental risk assessment, and the effectiveness of metabolomics in elucidating changes in metabolic pathways of aquatic organisms caged at sites differentially contaminated, and thus its suitability to be applied in ecotoxicological studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparative study of single/combination use of Huang-Lian-Jie-Du decoction and berberine on their protection on sepsis induced acute liver injury by NMR metabolic profiling.

PubMed

Lv, Yan; Wang, Junsong; Xu, Dingqiao; Liao, Shanting; Li, Pei; Zhang, Qian; Yang, Minghua; Kong, Lingyi

2017-10-25

Sepsis is a serious clinical disease with a high mortality rate all around the world. Liver organ dysfunction is an important sign for the severity and outcome of sepsis in patients. In this study, 1 H NMR-based metabolomics approach and biochemical assays were applied to investigate the metabolic profiling for cecal ligation and puncture (CLP) induced acute liver injury, the therapeutical effect of single/combination use of Huang-Lian-Jie-Du decoction (HLJDD) and berberine, and the interaction of them. Metabolomics analysis revealed significant perturbations in livers of septic rats, which could be ameliorated by HLJDD, berberine and their combination treatment. Berberine could better rectified glycolysis and nucleic acid metabolism in the liver. HLJDD had exceptional better anti-inflammatory, antibacterial and antioxidative effects than berberine. The interaction of berberine and HLJDD could further strengthen the anti-inflammation and anti-oxidation, but with poor effect on amino acids metabolism. These findings highlighted the feasibility of the integrated NMR based metabolomics approach to understand the pathogenesis of diseases, the action mechanisms of therapy and the herb-drug interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application.

PubMed

Klein, Matthias S; Shearer, Jane

2016-01-01

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

PubMed Central

Klein, Matthias S.; Shearer, Jane

2016-01-01

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine. PMID:26636104

GC-TOF/MS-based metabolomic profiling of estrogen deficiency-induced obesity in ovariectomized rats

PubMed Central

Ma, Bo; Zhang, Qi; Wang, Guang-ji; A, Ji-ye; Wu, Di; Liu, Ying; Cao, Bei; Liu, Lin-sheng; Hu, Ying-ying; Wang, Yong-lu; Zheng, Ya-ya

2011-01-01

Aim: To explore the alteration of endogenous metabolites and identify potential biomarkers using metabolomic profiling with gas chromatography coupled a time-of-flight mass analyzer (GC/TOF-MS) in a rat model of estrogen-deficiency-induced obesity. Methods: Twelve female Sprague-Dawley rats six month of age were either sham-operated or ovariectomized (OVX). Rat blood was collected, and serum was analyzed for biomarkers using standard colorimetric methods with commercial assay kits and a metabolomic approach with GC/TOF-MS. The data were analyzed using multivariate statistical techniques. Results: A high body weight and body mass index inversely correlated with serum estradiol (E2) in the OVX rats compared to the sham rats. Estrogen deficiency also significantly increased serum total cholesterol, triglycerides, and low-density lipoprotein cholesterol. Utilizing GC/TOF-MS-based metabolomic analysis and the partial least-squares discriminant analysis, the OVX samples were discriminated from the shams. Elevated levels of cholesterol, glycerol, glucose, arachidonic acid, glutamic acid, glycine, and cystine and reduced alanine levels were observed. Serum glucose metabolism, energy metabolism, lipid metabolism, and amino acid metabolism were involved in estrogen-deficiency-induced obesity in OVX rats. Conclusion: The series of potential biomarkers identified in the present study provided fingerprints of rat metabolomic changes during obesity and an overview of multiple metabolic pathways during the progression of obesity involving glucose metabolism, lipid metabolism, and amino acid metabolism. PMID:21293480

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress.

PubMed

Jorge, Tiago F; Rodrigues, João A; Caldana, Camila; Schmidt, Romy; van Dongen, Joost T; Thomas-Oates, Jane; António, Carla

2016-09-01

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016. © 2015 Wiley Periodicals, Inc.

Identifying biomarkers for asthma diagnosis using targeted metabolomics approaches.

PubMed

Checkley, William; Deza, Maria P; Klawitter, Jost; Romero, Karina M; Klawitter, Jelena; Pollard, Suzanne L; Wise, Robert A; Christians, Uwe; Hansel, Nadia N

2016-12-01

The diagnosis of asthma in children is challenging and relies on a combination of clinical factors and biomarkers including methacholine challenge, lung function, bronchodilator responsiveness, and presence of airway inflammation. No single test is diagnostic. We sought to identify a pattern of inflammatory biomarkers that was unique to asthma using a targeted metabolomics approach combined with data science methods. We conducted a nested case-control study of 100 children living in a peri-urban community in Lima, Peru. We defined cases as children with current asthma, and controls as children with no prior history of asthma and normal lung function. We further categorized enrollment following a factorial design to enroll equal numbers of children as either overweight or not. We obtained a fasting venous blood sample to characterize a comprehensive panel of targeted markers using a metabolomics approach based on high performance liquid chromatography-mass spectrometry. A statistical comparison of targeted metabolites between children with asthma (n = 50) and healthy controls (n = 49) revealed distinct patterns in relative concentrations of several metabolites: children with asthma had approximately 40-50% lower relative concentrations of ascorbic acid, 2-isopropylmalic acid, shikimate-3-phosphate, and 6-phospho-d-gluconate when compared to children without asthma, and 70% lower relative concentrations of reduced glutathione (all p < 0.001 after Bonferroni correction). Moreover, a combination of 2-isopropylmalic acid and betaine strongly discriminated between children with asthma (2-isopropylmalic acid ≤ 13 077 normalized counts/second) and controls (2-isopropylmalic acid > 13 077 normalized counts/second and betaine ≤ 16 47 121 normalized counts/second). By using a metabolomics approach applied to serum, we were able to discriminate between children with and without asthma by revealing different metabolic patterns. These results suggest that serum metabolomics may represent a diagnostic tool for asthma and may be helpful for distinguishing asthma phenotypes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Counting missing values in a metabolite-intensity data set for measuring the analytical performance of a metabolomics platform.

PubMed

Huan, Tao; Li, Liang

2015-01-20

Metabolomics requires quantitative comparison of individual metabolites present in an entire sample set. Unfortunately, missing intensity values in one or more samples are very common. Because missing values can have a profound influence on metabolomic results, the extent of missing values found in a metabolomic data set should be treated as an important parameter for measuring the analytical performance of a technique. In this work, we report a study on the scope of missing values and a robust method of filling the missing values in a chemical isotope labeling (CIL) LC-MS metabolomics platform. Unlike conventional LC-MS, CIL LC-MS quantifies the concentration differences of individual metabolites in two comparative samples based on the mass spectral peak intensity ratio of a peak pair from a mixture of differentially labeled samples. We show that this peak-pair feature can be explored as a unique means of extracting metabolite intensity information from raw mass spectra. In our approach, a peak-pair peaking algorithm, IsoMS, is initially used to process the LC-MS data set to generate a CSV file or table that contains metabolite ID and peak ratio information (i.e., metabolite-intensity table). A zero-fill program, freely available from MyCompoundID.org , is developed to automatically find a missing value in the CSV file and go back to the raw LC-MS data to find the peak pair and, then, calculate the intensity ratio and enter the ratio value into the table. Most of the missing values are found to be low abundance peak pairs. We demonstrate the performance of this method in analyzing an experimental and technical replicate data set of human urine metabolome. Furthermore, we propose a standardized approach of counting missing values in a replicate data set as a way of gauging the extent of missing values in a metabolomics platform. Finally, we illustrate that applying the zero-fill program, in conjunction with dansylation CIL LC-MS, can lead to a marked improvement in finding significant metabolites that differentiate bladder cancer patients and their controls in a metabolomics study of 109 subjects.

Evaluation of the Nutritional Quality of Chinese Kale (Brassica alboglabra Bailey) Using UHPLC-Quadrupole-Orbitrap MS/MS-Based Metabolomics.

PubMed

Wang, Ya-Qin; Hu, Li-Ping; Liu, Guang-Min; Zhang, De-Shuang; He, Hong-Ju

2017-07-27

Chinese kale ( Brassica alboglabra Bailey) is a widely consumed vegetable which is rich in antioxidants and anticarcinogenic compounds. Herein, we used an untargeted ultra-high-performance liquid chromatography (UHPLC)-Quadrupole-Orbitrap MS/MS-based metabolomics strategy to study the nutrient profiles of Chinese kale. Seven Chinese kale cultivars and three different edible parts were evaluated, and amino acids, sugars, organic acids, glucosinolates and phenolic compounds were analysed simultaneously. We found that two cultivars, a purple-stem cultivar W1 and a yellow-flower cultivar Y1, had more health-promoting compounds than others. The multivariate statistical analysis results showed that gluconapin was the most important contributor for discriminating both cultivars and edible parts. The purple-stem cultivar W1 had higher levels of some phenolic acids and flavonoids than the green stem cultivars. Compared to stems and leaves, the inflorescences contained more amino acids, glucosinolates and most of the phenolic acids. Meanwhile, the stems had the least amounts of phenolic compounds among the organs tested. Metabolomics is a powerful approach for the comprehensive understanding of vegetable nutritional quality. The results provide the basis for future metabolomics-guided breeding and nutritional quality improvement.

Metabolome and proteome profiling of complex I deficiency induced by rotenone.

PubMed

Gielisch, Ina; Meierhofer, David

2015-01-02

Complex I (CI; NADH dehydrogenase) deficiency causes mitochondrial diseases, including Leigh syndrome. A variety of clinical symptoms of CI deficiency are known, including neurodegeneration. Here, we report an integrative study combining liquid chromatography-mass spectrometry (LC-MS)-based metabolome and proteome profiling in CI deficient HeLa cells. We report a rapid LC-MS-based method for the relative quantification of targeted metabolome profiling with an additional layer of confidence by applying multiple reaction monitoring (MRM) ion ratios for further identity confirmation and robustness. The proteome was analyzed by label-free quantification (LFQ). More than 6000 protein groups were identified. Pathway and network analyses revealed that the respiratory chain was highly deregulated, with metabolites such as FMN, FAD, NAD(+), and ADP, direct players of the OXPHOS system, and metabolites of the TCA cycle decreased up to 100-fold. Synthesis of functional iron-sulfur clusters, which are of central importance for the electron transfer chain, and degradation products like bilirubin were also significantly reduced. Glutathione metabolism on the pathway level, as well as individual metabolite components such as NADPH, glutathione (GSH), and oxidized glutathione (GSSG), was downregulated. Overall, metabolome and proteome profiles in CI deficient cells correlated well, supporting our integrated approach.

Advances in high-resolution mass spectrometry based on metabolomics studies for food--a review.

PubMed

Rubert, Josep; Zachariasova, Milena; Hajslova, Jana

2015-01-01

Food authenticity becomes a necessity for global food policies, since food placed in the market without fail has to be authentic. It has always been a challenge, since in the past minor components, called also markers, have been mainly monitored by chromatographic methods in order to authenticate the food. Nevertheless, nowadays, advanced analytical methods have allowed food fingerprints to be achieved. At the same time they have been also combined with chemometrics, which uses statistical methods in order to verify food and to provide maximum information by analysing chemical data. These sophisticated methods based on different separation techniques or stand alone have been recently coupled to high-resolution mass spectrometry (HRMS) in order to verify the authenticity of food. The new generation of HRMS detectors have experienced significant advances in resolving power, sensitivity, robustness, extended dynamic range, easier mass calibration and tandem mass capabilities, making HRMS more attractive and useful to the food metabolomics community, therefore becoming a reliable tool for food authenticity. The purpose of this review is to summarise and describe the most recent metabolomics approaches in the area of food metabolomics, and to discuss the strengths and drawbacks of the HRMS analytical platforms combined with chemometrics.

GC-MS-Based Metabolome and Metabolite Regulation in Serum-Resistant Streptococcus agalactiae.

PubMed

Wang, Zhe; Li, Min-Yi; Peng, Bo; Cheng, Zhi-Xue; Li, Hui; Peng, Xuan-Xian

2016-07-01

Streptococcus agalactiae causes severe systemic infections in human and fish. In the present study, we established a pathogen-plasma interaction model by which we explored how S. agalactiae evaded serum-mediated killing. We found that S. agalactiae grew faster in the presence of yellow grouper plasma than in the absence of the plasma, indicating S. agalactiae evolved a way of evading the fish immune system. To determine the events underlying this phenotype, we applied GC-MS-based metabolomics approaches to identify differential metabolomes between S. agalactiae cultured with and without yellow grouper plasma. Through bioinformatics analysis, decreased malic acid and increased adenosine were identified as the most crucial metabolites that distinguish the two groups. Meanwhile, they presented with decreased TCA cycle and elevated purine metabolism, respectively. Finally, exogenous malic acid and adenosine were used to reprogram the plasma-resistant metabolome, leading to elevated and decreased susceptibility to the plasma, respectively. Therefore, our findings reveal for the first time that S. agalactiae utilizes a metabolic trick to respond to plasma killing as a result of serum resistance, which may be reverted or enhanced by exogenous malic acid and adenosine, respectively, suggesting that the metabolic trick can be regulated by metabolites.

Metabolomics approach reveals metabolic disorders and potential biomarkers associated with the developmental toxicity of tetrabromobisphenol A and tetrachlorobisphenol A

NASA Astrophysics Data System (ADS)

Ye, Guozhu; Chen, Yajie; Wang, Hong-Ou; Ye, Ting; Lin, Yi; Huang, Qiansheng; Chi, Yulang; Dong, Sijun

2016-10-01

Tetrabromobisphenol A and tetrachlorobisphenol A are halogenated bisphenol A (H-BPA), and has raised concerns about their adverse effects on the development of fetuses and infants, however, the molecular mechanisms are unclear, and related metabolomics studies are limited. Accordingly, a metabolomics study based on gas chromatography-mass spectrometry was employed to elucidate the molecular developmental toxicology of H-BPA using the marine medaka (Oryzias melastigmas) embryo model. Here, we revealed decreased synthesis of nucleosides, amino acids and lipids, and disruptions in the TCA (tricarboxylic acid) cycle, glycolysis and lipid metabolism, thus inhibiting the developmental processes of embryos exposed to H-BPA. Unexpectedly, we observed enhanced neural activity accompanied by lactate accumulation and accelerated heart rates due to an increase in dopamine pathway and a decrease in inhibitory neurotransmitters following H-BPA exposure. Notably, disorders of the neural system, and disruptions in glycolysis, the TCA cycle, nucleoside metabolism, lipid metabolism, glutamate and aspartate metabolism induced by H-BPA exposure were heritable. Furthermore, lactate and dopa were identified as potential biomarkers of the developmental toxicity of H-BPA and related genetic effects. This study has demonstrated that the metabolomics approach is a useful tool for obtaining comprehensive and novel insights into the molecular developmental toxicity of environmental pollutants.

A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

PubMed Central

Hajduk, Joanna; Klupczynska, Agnieszka; Dereziński, Paweł; Matysiak, Jan; Kokot, Piotr; Nowak, Dorota M.; Gajęcka, Marzena; Nowak-Markwitz, Ewa; Kokot, Zenon J.

2015-01-01

The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18) and a matched control group (n = 13). The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, l-citrulline, l-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44%) and specificity (84.62%), as well as the total group membership classification value (90.32%) calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases. PMID:26694367

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles.

PubMed

Rácz, Anita; Andrić, Filip; Bajusz, Dávid; Héberger, Károly

2018-01-01

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

Comprehensive untargeted metabolomics of Lychnnophorinae subtribe (Asteraceae: Vernonieae) in a phylogenetic context.

PubMed

Martucci, Maria Elvira Poleti; Loeuille, Benoit; Pirani, José Rubens; Gobbo-Neto, Leonardo

2018-01-01

Members of the subtribe Lychnophorinae occur mostly within the Cerrado domain of the Brazilian Central Plateau. The relationships between its 11 genera, as well as between Lychnophorinae and other subtribes belonging to the tribe Vernonieae, have recently been investigated upon a phylogeny based on molecular and morphological data. We report the use of a comprehensive untargeted metabolomics approach, combining HPLC-MS and GC-MS data, followed by multivariate analyses aiming to assess the congruence between metabolomics data and the phylogenetic hypothesis, as well as its potential as a chemotaxonomic tool. We analyzed 78 species by UHPLC-MS and GC-MS in both positive and negative ionization modes. The metabolic profiles obtained for these species were treated in MetAlign and in MSClust and the matrices generated were used in SIMCA for hierarchical cluster analyses, principal component analyses and orthogonal partial least square discriminant analysis. The results showed that metabolomic analyses are mostly congruent with the phylogenetic hypothesis especially at lower taxonomic levels (Lychnophora or Eremanthus). Our results confirm that data generated using metabolomics provide evidence for chemotaxonomical studies, especially for phylogenetic inference of the Lychnophorinae subtribe and insight into the evolution of the secondary metabolites of this group.

Fecal metabolome of the Hadza hunter-gatherers: a host-microbiome integrative view

PubMed Central

Turroni, Silvia; Fiori, Jessica; Rampelli, Simone; Schnorr, Stephanie L.; Consolandi, Clarissa; Barone, Monica; Biagi, Elena; Fanelli, Flaminia; Mezzullo, Marco; Crittenden, Alyssa N.; Henry, Amanda G.; Brigidi, Patrizia; Candela, Marco

2016-01-01

The recent characterization of the gut microbiome of traditional rural and foraging societies allowed us to appreciate the essential co-adaptive role of the microbiome in complementing our physiology, opening up significant questions on how the microbiota changes that have occurred in industrialized urban populations may have altered the microbiota-host co-metabolic network, contributing to the growing list of Western diseases. Here, we applied a targeted metabolomics approach to profile the fecal metabolome of the Hadza of Tanzania, one of the world’s few remaining foraging populations, and compared them to the profiles of urban living Italians, as representative of people in the post-industrialized West. Data analysis shows that during the rainy season, when the diet is primarily plant-based, Hadza are characterized by a distinctive enrichment in hexoses, glycerophospholipids, sphingolipids, and acylcarnitines, while deplete in the most common natural amino acids and derivatives. Complementary to the documented unique metagenomic features of their gut microbiome, our findings on the Hadza metabolome lend support to the notion of an alternate microbiome configuration befitting of a nomadic forager lifestyle, which helps maintain metabolic homeostasis through an overall scarcity of inflammatory factors, which are instead highly represented in the Italian metabolome. PMID:27624970

Comprehensive untargeted metabolomics of Lychnnophorinae subtribe (Asteraceae: Vernonieae) in a phylogenetic context

PubMed Central

Martucci, Maria Elvira Poleti; Loeuille, Benoit; Pirani, José Rubens

2018-01-01

Members of the subtribe Lychnophorinae occur mostly within the Cerrado domain of the Brazilian Central Plateau. The relationships between its 11 genera, as well as between Lychnophorinae and other subtribes belonging to the tribe Vernonieae, have recently been investigated upon a phylogeny based on molecular and morphological data. We report the use of a comprehensive untargeted metabolomics approach, combining HPLC-MS and GC-MS data, followed by multivariate analyses aiming to assess the congruence between metabolomics data and the phylogenetic hypothesis, as well as its potential as a chemotaxonomic tool. We analyzed 78 species by UHPLC-MS and GC-MS in both positive and negative ionization modes. The metabolic profiles obtained for these species were treated in MetAlign and in MSClust and the matrices generated were used in SIMCA for hierarchical cluster analyses, principal component analyses and orthogonal partial least square discriminant analysis. The results showed that metabolomic analyses are mostly congruent with the phylogenetic hypothesis especially at lower taxonomic levels (Lychnophora or Eremanthus). Our results confirm that data generated using metabolomics provide evidence for chemotaxonomical studies, especially for phylogenetic inference of the Lychnophorinae subtribe and insight into the evolution of the secondary metabolites of this group. PMID:29324799

Application of a novel metabolomic approach based on atmospheric pressure photoionization mass spectrometry using flow injection analysis for the study of Alzheimer's disease.

PubMed

González-Domínguez, Raúl; García-Barrera, Tamara; Gómez-Ariza, José Luis

2015-01-01

The use of atmospheric pressure photoionization is not widespread in metabolomics, despite its considerable potential for the simultaneous analysis of compounds with diverse polarities. This work considers the development of a novel analytical approach based on flow injection analysis and atmospheric pressure photoionization mass spectrometry for rapid metabolic screening of serum samples. Several experimental parameters were optimized, such as type of dopant, flow injection solvent, and their flows, given that a careful selection of these variables is mandatory for a comprehensive analysis of metabolites. Toluene and methanol were the most suitable dopant and flow injection solvent, respectively. Moreover, analysis in negative mode required higher solvent and dopant flows (100 µl min(-1) and 40 µl min(-1), respectively) compared to positive mode (50 µl min(-1) and 20 µl min(-1)). Then, the optimized approach was used to elucidate metabolic alterations associated with Alzheimer's disease. Thereby, results confirm the increase of diacylglycerols, ceramides, ceramide-1-phosphate and free fatty acids, indicating membrane destabilization processes, and reduction of fatty acid amides and several neurotransmitters related to impairments in neuronal transmission, among others. Therefore, it could be concluded that this metabolomic tool presents a great potential for analysis of biological samples, considering its high-throughput screening capability, fast analysis and comprehensive metabolite coverage. Copyright © 2014 Elsevier B.V. All rights reserved.

NMR-based metabolomics approach to study the toxicity of lambda-cyhalothrin to goldfish (Carassius auratus).

PubMed

Li, Minghui; Wang, Junsong; Lu, Zhaoguang; Wei, Dandan; Yang, Minghua; Kong, Lingyi

2014-01-01

In this study, a (1)H nuclear magnetic resonance (NMR) based metabolomics approach was applied to investigate the toxicity of lambda-cyhalothrin (LCT) in goldfish (Carassius auratus). LCT showed tissue-specific damage to gill, heart, liver and kidney tissues of goldfish. NMR profiling combined with statistical methods such as orthogonal partial least squares discriminant analysis (OPLS-DA) and two-dimensional statistical total correlation spectroscopy (2D-STOCSY) was developed to discern metabolite changes occurring after one week LCT exposure in brain, heart and kidney tissues of goldfish. LCT exposure influenced levels of many metabolites (e.g., leucine, isoleucine and valine in brain and kidney; lactate in brain, heart and kidney; alanine in brain and kidney; choline in brain, heart and kidney; taurine in brain, heart and kidney; N-acetylaspartate in brain; myo-inositol in brain; phosphocreatine in brain and heart; 2-oxoglutarate in brain; cis-aconitate in brain, and etc.), and broke the balance of neurotransmitters and osmoregulators, evoked oxidative stress, disturbed metabolisms of energy and amino acids. The implication of glutamate-glutamine-gamma-aminobutyric axis in LCT induced toxicity was demonstrated for the first time. Our findings demonstrated the applicability and potential of metabolomics approach for the elucidation of toxicological effects of pesticides and the underlying mechanisms, and the discovery of biomarkers for pesticide pollution in aquatic environment. Copyright © 2013 Elsevier B.V. All rights reserved.

Applications of Metabolomics in Cancer Studies.

PubMed

Armitage, Emily Grace; Ciborowski, Michal

2017-01-01

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases.

PubMed

Gomez-Casati, Diego F; Zanor, Maria I; Busi, María V

2013-01-01

In the recent years, there has been an increase in the number of metabolomic approaches used, in parallel with proteomic and functional genomic studies. The wide variety of chemical types of metabolites available has also accelerated the use of different techniques in the investigation of the metabolome. At present, metabolomics is applied to investigate several human diseases, to improve their diagnosis and prevention, and to design better therapeutic strategies. In addition, metabolomic studies are also being carried out in areas such as toxicology and pharmacology, crop breeding, and plant biotechnology. In this review, we emphasize the use and application of metabolomics in human diseases and plant research to improve human health.

Metabolomics for laboratory diagnostics.

PubMed

Bujak, Renata; Struck-Lewicka, Wiktoria; Markuszewski, Michał J; Kaliszan, Roman

2015-09-10

Metabolomics is an emerging approach in a systems biology field. Due to continuous development in advanced analytical techniques and in bioinformatics, metabolomics has been extensively applied as a novel, holistic diagnostic tool in clinical and biomedical studies. Metabolome's measurement, as a chemical reflection of a current phenotype of a particular biological system, is nowadays frequently implemented to understand pathophysiological processes involved in disease progression as well as to search for new diagnostic or prognostic biomarkers of various organism's disorders. In this review, we discussed the research strategies and analytical platforms commonly applied in the metabolomics studies. The applications of the metabolomics in laboratory diagnostics in the last 5 years were also reviewed according to the type of biological sample used in the metabolome's analysis. We also discussed some limitations and further improvements which should be considered taking in mind potential applications of metabolomic research and practice. Copyright © 2014 Elsevier B.V. All rights reserved.

Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment

PubMed Central

Hirai, Tadayoshi; Oikawa, Akira; Matsuda, Fumio; Fukushima, Atsushi; Arita, Masanori; Watanabe, Shin; Yano, Megumu; Hiwasa-Tanase, Kyoko; Ezura, Hiroshi; Saito, Kazuki

2011-01-01

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms. PMID:21359231

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE PAGES

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.; ...

2018-01-17

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

PubMed Central

Hoyt, David W.; Nicora, Carrie D.; Kinmonth-Schultz, Hannah A.; Ward, Joy K.

2018-01-01

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. PMID:29342073

Metabolomics Analysis of Metabolic Effects of Nicotinamide Phosphoribosyltransferase (NAMPT) Inhibition on Human Cancer Cells

PubMed Central

Tolstikov, Vladimir; Nikolayev, Alexander; Dong, Sucai; Zhao, Genshi; Kuo, Ming-Shang

2014-01-01

Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide–consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry–based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level. PMID:25486521

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bingol, Kerem

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome manual absolute quantitation step of metabolites in 1D 1H NMR spectra. This provides more consistency between inter-laboratory comparisons. Integration of 2D NMR metabolomics databases under a unified web server allowed very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMRmore » and mass spectrometry. These hybrid NMR/MS approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing to profile ever larger number of metabolites in application studies.« less

An Overview on the Importance of Combining Complementary Analytical Platforms in Metabolomic Research.

PubMed

Gonzalez-Dominguez, Alvaro; Duran-Guerrero, Enrique; Fernandez-Recamales, Angeles; Lechuga-Sancho, Alfonso Maria; Sayago, Ana; Schwarz, Monica; Segundo, Carmen; Gonzalez-Dominguez, Raul

2017-01-01

The analytical bias introduced by most of the commonly used techniques in metabolomics considerably hinders the simultaneous detection of all metabolites present in complex biological samples. In order to solve this limitation, the combination of complementary approaches is emerging in recent years as the most suitable strategy in order to maximize metabolite coverage. This review article presents a general overview of the most important analytical techniques usually employed in metabolomics: nuclear magnetic resonance, mass spectrometry and hybrid approaches. Furthermore, we emphasize the potential of integrating various tools in the form of metabolomic multi-platforms in order to get a deeper metabolome characterization, for which a revision of the existing literature in this field is provided. This review is not intended to be exhaustive but, rather, to give a practical and concise guide to readers not familiar with analytical chemistry on the considerations to account for the proper selection of the technique to be used in a metabolomic experiment in biomedical research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Fusing metabolomics data sets with heterogeneous measurement errors

PubMed Central

Waaijenborg, Sandra; Korobko, Oksana; Willems van Dijk, Ko; Lips, Mirjam; Hankemeier, Thomas; Wilderjans, Tom F.; Smilde, Age K.

2018-01-01

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups. PMID:29698490

Metabolome analysis of 20 taxonomically related benzylisoquinoline alkaloid-producing plants.

PubMed

Hagel, Jillian M; Mandal, Rupasri; Han, Beomsoo; Han, Jun; Dinsmore, Donald R; Borchers, Christoph H; Wishart, David S; Facchini, Peter J

2015-09-15

Recent progress toward the elucidation of benzylisoquinoline alkaloid (BIA) metabolism has focused on a small number of model plant species. Current understanding of BIA metabolism in plants such as opium poppy, which accumulates important pharmacological agents such as codeine and morphine, has relied on a combination of genomics and metabolomics to facilitate gene discovery. Metabolomics studies provide important insight into the primary biochemical networks underpinning specialized metabolism, and serve as a key resource for metabolic engineering, gene discovery, and elucidation of governing regulatory mechanisms. Beyond model plants, few broad-scope metabolomics reports are available for the vast number of plant species known to produce an estimated 2500 structurally diverse BIAs, many of which exhibit promising medicinal properties. We applied a multi-platform approach incorporating four different analytical methods to examine 20 non-model, BIA-accumulating plant species. Plants representing four families in the Ranunculales were chosen based on reported BIA content, taxonomic distribution and importance in modern/traditional medicine. One-dimensional (1)H NMR-based profiling quantified 91 metabolites and revealed significant species- and tissue-specific variation in sugar, amino acid and organic acid content. Mono- and disaccharide sugars were generally lower in roots and rhizomes compared with stems, and a variety of metabolites distinguished callus tissue from intact plant organs. Direct flow infusion tandem mass spectrometry provided a broad survey of 110 lipid derivatives including phosphatidylcholines and acylcarnitines, and high-performance liquid chromatography coupled with UV detection quantified 15 phenolic compounds including flavonoids, benzoic acid derivatives and hydroxycinnamic acids. Ultra-performance liquid chromatography coupled with high-resolution Fourier transform mass spectrometry generated extensive mass lists for all species, which were mined for metabolites putatively corresponding to BIAs. Different alkaloids profiles, including both ubiquitous and potentially rare compounds, were observed. Extensive metabolite profiling combining multiple analytical platforms enabled a more complete picture of overall metabolism occurring in selected plant species. This study represents the first time a metabolomics approach has been applied to most of these species, despite their importance in modern and traditional medicine. Coupled with genomics data, these metabolomics resources serve as a key resource for the investigation of BIA biosynthesis in non-model plant species.

Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data.

PubMed

Reisetter, Anna C; Muehlbauer, Michael J; Bain, James R; Nodzenski, Michael; Stevens, Robert D; Ilkayeva, Olga; Metzger, Boyd E; Newgard, Christopher B; Lowe, William L; Scholtens, Denise M

2017-02-02

Metabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds. To curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability, we propose mixture model normalization (mixnorm) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples. Mixnorm outperforms other approaches across many metrics, including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch. For some metrics, particularly when truncation is less frequent for a metabolite, mean centering and median scaling demonstrate comparable performance to mixnorm. When quality control samples are systematically included in batches, mixnorm is uniquely suited to normalizing non-targeted GC/MS metabolomics data due to explicit accommodation of batch effects, run order and varying thresholds of detectability. Especially in large-scale studies, normalization is crucial for drawing accurate conclusions from non-targeted GC/MS metabolomics data.

What Have Metabolomics Approaches Taught Us About Type 2 Diabetes?

PubMed

Gonzalez-Franquesa, Alba; Burkart, Alison M; Isganaitis, Elvira; Patti, Mary-Elizabeth

2016-08-01

Type 2 diabetes (T2D) is increasing worldwide, making identification of biomarkers for detection, staging, and effective prevention strategies an especially critical scientific and medical goal. Fortunately, advances in metabolomics techniques, together with improvements in bioinformatics and mathematical modeling approaches, have provided the scientific community with new tools to describe the T2D metabolome. The metabolomics signatures associated with T2D and obesity include increased levels of lactate, glycolytic intermediates, branched-chain and aromatic amino acids, and long-chain fatty acids. Conversely, tricarboxylic acid cycle intermediates, betaine, and other metabolites decrease. Future studies will be required to fully integrate these and other findings into our understanding of diabetes pathophysiology and to identify biomarkers of disease risk, stage, and responsiveness to specific treatments.

Linkage of exposure and effects using genomics, proteomics and metabolomics in small fish models (presentation)

EPA Science Inventory

This research project combines the use of whole organism endpoints, genomic, proteomic and metabolomic approaches, and computational modeling in a systems biology approach to 1) identify molecular indicators of exposure and biomarkers of effect to EDCs representing several modes/...

Metabolomics and Epidemiology Working Group

Cancer.gov

The Metabolomics and Epidemiology (MetEpi) Working Group promotes metabolomics analyses in population-based studies, as well as advancement in the field of metabolomics for broader biomedical and public health research.

Metabolomics and Personalized Medicine.

PubMed

Koen, Nadia; Du Preez, Ilse; Loots, Du Toit

2016-01-01

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine. © 2016 Elsevier Inc. All rights reserved.

Unraveling Metabolic Variation for Blueberry and Chokeberry Cultivars Harvested from Different Geo-Climatic Regions in Korea.

PubMed

Sim, Inseon; Suh, Dong Ho; Singh, Digar; Do, Seon-Gil; Moon, Kwang Hyun; Lee, Jeong Ho; Ku, Kang-Mo; Lee, Choong Hwan

2017-10-18

Temporal geo-climatic variations are presumably vital determinants of phenotypic traits and quality characteristics of berries manifested through reconfigured metabolomes. We performed an untargeted mass spectrometry (MS)-based metabolomic analysis of blueberry (Vaccinium spp.) and chokeberry (Aronia melanocarpa) sample extracts harvested from different geo-climatic regions in Korea. The multivariate statistical analysis indicated distinct metabolite compositions of berry groups based on different species and regions. The amino acids levels were relatively more abundant in chokeberry than in blueberry, while the sugar contents were comparatively higher in blueberry. However, the metabolite compositions were also dependent on geo-climatic conditions, especially latitude. Notwithstanding the cultivar types, amino acids, and sucrose were relatively more abundant in berries harvested from 35°N and 36°N geo-climatic regions, respectively, characterized by distinct duration of sunshine and rainfall patterns. The present study showed the ability of a metabolomics approach for recapitulating the significance of geo-climatic parameters for quality characterization of commercial berry types.

Revisiting the Metabolism and Bioactivation of Ketoconazole in Human and Mouse Using Liquid Chromatography–Mass Spectrometry-Based Metabolomics

PubMed Central

Kim, Ju-Hyun; Choi, Won-Gu; Lee, Sangkyu; Lee, Hye Suk

2017-01-01

Although ketoconazole (KCZ) has been used worldwide for 30 years, its metabolic characteristics are poorly described. Moreover, the hepatotoxicity of KCZ limits its therapeutic use. In this study, we used liquid chromatography–mass spectrometry-based metabolomics to evaluate the metabolic profile of KCZ in mouse and human and identify the mechanisms underlying its hepatotoxicity. A total of 28 metabolites of KCZ, 11 of which were novel, were identified in this study. Newly identified metabolites were classified into three categories according to the metabolic positions of a piperazine ring, imidazole ring, and N-acetyl moiety. The metabolic characteristics of KCZ in human were comparable to those in mouse. Moreover, three cyanide adducts of KCZ were identified in mouse and human liver microsomal incubates as “flags” to trigger additional toxicity study. The oxidation of piperazine into iminium ion is suggested as a biotransformation responsible for bioactivation. In summary, the metabolic characteristics of KCZ, including reactive metabolites, were comprehensively understood using a metabolomics approach. PMID:28335386

1H NMR-based metabolomics study of liver damage induced by ginkgolic acid (15:1) in mice.

PubMed

Jiang, Lei; Si, Zhi-Hong; Li, Ming-Hui; Zhao, He; Fu, Yong-Hong; Xing, Yue-Xiao; Hong, Wei; Ruan, Ling-Yu; Li, Pu-Min; Wang, Jun-Song

2017-03-20

Ginkgolic acid (15:1) is a major toxic component in extracts obtained from Ginkgo biloba (EGb) that has allergic and genotoxic effects. This study is the first to explore the hepatotoxicity of ginkgolic acid (15:1) using a NMR (nuclear magnetic resonance)-based metabolomics approach in combination with biochemistry assays. Mice were orally administered two doses of ginkgolic acid (15:1), and mouse livers and serum were then collected for NMR recordings and biochemical assays. The levels of activity of alanine aminotransferase (ALT) and glutamic aspartate transaminase (AST) observed in the ginkgolic acid (15:1)-treated mice suggested that it had induced severe liver damage. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) performed to determine the metabolomic profile of mouse liver tissues indicated that many metabolic disturbances, especially oxidative stress and purine metabolism, were induced by ginkgolic acid (15:1). A correlation network analysis combined with information related to structural similarities further confirmed that purine metabolism was disturbed by ginkgolic acid (15:1). This mechanism might represent the link between the antitumour activity and the liver injury-inducing effect of ginkgolic acid (15:1). A SUS (Shared and Unique Structure) plot suggested that a two-dose treatment of ginkgolic acid (15:1) had generally the same effect on metabolic variations but that its effects were dose-dependent, revealing some of the common features of ginkgolic acid (15:1) dosing. This integrated metabolomics approach helped us to characterise ginkgolic acid (15:1)-induced liver damage in mice. Copyright © 2016 Elsevier B.V. All rights reserved.

Metabolomics of Oxidative Stress in Recent Studies of Endogenous and Exogenously Administered Intermediate Metabolites

PubMed Central

Liu, Jia; Litt, Lawrence; Segal, Mark R.; Kelly, Mark J. S.; Pelton, Jeffrey G.; Kim, Myungwon

2011-01-01

Aerobic metabolism occurs in a background of oxygen radicals and reactive oxygen species (ROS) that originate from the incomplete reduction of molecular oxygen in electron transfer reactions. The essential role of aerobic metabolism, the generation and consumption of ATP and other high energy phosphates, sustains a balance of approximately 3000 essential human metabolites that serve not only as nutrients, but also as antioxidants, neurotransmitters, osmolytes, and participants in ligand-based and other cellular signaling. In hypoxia, ischemia, and oxidative stress, where pathological circumstances cause oxygen radicals to form at a rate greater than is possible for their consumption, changes in the composition of metabolite ensembles, or metabolomes, can be associated with physiological changes. Metabolomics and metabonomics are a scientific disciplines that focuse on quantifying dynamic metabolome responses, using multivariate analytical approaches derived from methods within genomics, a discipline that consolidated innovative analysis techniques for situations where the number of biomarkers (metabolites in our case) greatly exceeds the number of subjects. This review focuses on the behavior of cytosolic, mitochondrial, and redox metabolites in ameliorating or exacerbating oxidative stress. After reviewing work regarding a small number of metabolites—pyruvate, ethyl pyruvate, and fructose-1,6-bisphosphate—whose exogenous administration was found to ameliorate oxidative stress, a subsequent section reviews basic multivariate statistical methods common in metabolomics research, and their application in human and preclinical studies emphasizing oxidative stress. Particular attention is paid to new NMR spectroscopy methods in metabolomics and metabonomics. Because complex relationships connect oxidative stress to so many physiological processes, studies from different disciplines were reviewed. All, however, shared the common goal of ultimately developing “omics”-based, diagnostic tests to help influence therapies. PMID:22072900

Metabolomics: beyond biomarkers and towards mechanisms

PubMed Central

Johnson, Caroline H.; Ivanisevic, Julijana; Siuzdak, Gary

2017-01-01

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases. PMID:26979502

The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics.

PubMed

Gong, Zhi-Gang; Hu, Jing; Wu, Xi; Xu, Yong-Jiang

2017-07-04

Metabolomics is a critical member in systems biology. Although great progress has been achieved in metabolomics, there are still some problems in sample preparation, data processing and data interpretation. In this review, we intend to explore the roles, challenges and trends in sample preparation for mass spectrometry- (MS-) based metabolomics. The newly emerged sample preparation methods were also critically examined, including laser microdissection, in vivo sampling, dried blood spot, microwave, ultrasound and enzyme-assisted extraction, as well as microextraction techniques. Finally, we provide some conclusions and perspectives for sample preparation in MS-based metabolomics.

The food metabolome: a window over dietary exposure.

PubMed

Scalbert, Augustin; Brennan, Lorraine; Manach, Claudine; Andres-Lacueva, Cristina; Dragsted, Lars O; Draper, John; Rappaport, Stephen M; van der Hooft, Justin J J; Wishart, David S

2014-06-01

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health. © 2014 American Society for Nutrition.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

PubMed

Cavill, Rachel; Kamburov, Atanas; Ellis, James K; Athersuch, Toby J; Blagrove, Marcus S C; Herwig, Ralf; Ebbels, Timothy M D; Keun, Hector C

2011-03-01

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Expanding the Described Metabolome of the Marine Cyanobacterium Moorea producens JHB through Orthogonal Natural Products Workflows

PubMed Central

Boudreau, Paul D.; Monroe, Emily A.; Mehrotra, Suneet; Desfor, Shane; Korobeynikov, Anton; Sherman, David H.; Murray, Thomas F.; Gerwick, Lena; Dorrestein, Pieter C.; Gerwick, William H.

2015-01-01

Moorea producens JHB, a Jamaican strain of tropical filamentous marine cyanobacteria, has been extensively studied by traditional natural products techniques. These previous bioassay and structure guided isolations led to the discovery of two exciting classes of natural products, hectochlorin (1) and jamaicamides A (2) and B (3). In the current study, mass spectrometry-based ‘molecular networking’ was used to visualize the metabolome of Moorea producens JHB, and both guided and enhanced the isolation workflow, revealing additional metabolites in these compound classes. Further, we developed additional insight into the metabolic capabilities of this strain by genome sequencing analysis, which subsequently led to the isolation of a compound unrelated to the jamaicamide and hectochlorin families. Another approach involved stimulation of the biosynthesis of a minor jamaicamide metabolite by cultivation in modified media, and provided insights about the underlying biosynthetic machinery as well as preliminary structure-activity information within this structure class. This study demonstrated that these orthogonal approaches are complementary and enrich secondary metabolomic coverage even in an extensively studied bacterial strain. PMID:26222584

Evaluation of Normalization Methods to Pave the Way Towards Large-Scale LC-MS-Based Metabolomics Profiling Experiments

PubMed Central

Valkenborg, Dirk; Baggerman, Geert; Vanaerschot, Manu; Witters, Erwin; Dujardin, Jean-Claude; Burzykowski, Tomasz; Berg, Maya

2013-01-01

Abstract Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments. PMID:23808607

Development of a Postcolumn Infused-Internal Standard Liquid Chromatography Mass Spectrometry Method for Quantitative Metabolomics Studies.

PubMed

Liao, Hsiao-Wei; Chen, Guan-Yuan; Wu, Ming-Shiang; Liao, Wei-Chih; Lin, Ching-Hung; Kuo, Ching-Hua

2017-02-03

Quantitative metabolomics has become much more important in clinical research in recent years. Individual differences in matrix effects (MEs) and the injection order effect are two major factors that reduce the quantification accuracy in liquid chromatography-electrospray ionization-mass spectrometry-based (LC-ESI-MS) metabolomics studies. This study proposed a postcolumn infused-internal standard (PCI-IS) combined with a matrix normalization factor (MNF) strategy to improve the analytical accuracy of quantitative metabolomics. The PCI-IS combined with the MNF method was applied for a targeted metabolomics study of amino acids (AAs). D8-Phenylalanine was used as the PCI-IS, and it was postcolumn-infused into the ESI interface for calibration purposes. The MNF was used to bridge the AA response in a standard solution with the plasma samples. The MEs caused signal changes that were corrected by dividing the AA signal intensities by the PCI-IS intensities after adjustment with the MNF. After the method validation, we evaluated the method applicability for breast cancer research using 100 plasma samples. The quantification results revealed that the 11 tested AAs exhibit an accuracy between 88.2 and 110.7%. The principal component analysis score plot revealed that the injection order effect can be successfully removed, and most of the within-group variation of the tested AAs decreased after the PCI-IS correction. Finally, targeted metabolomics studies on the AAs showed that tryptophan was expressed more in malignant patients than in the benign group. We anticipate that a similar approach can be applied to other endogenous metabolites to facilitate quantitative metabolomics studies.

MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles

PubMed Central

2011-01-01

Background Improvements in the techniques for metabolomics analyses and growing interest in metabolomic approaches are resulting in the generation of increasing numbers of metabolomic profiles. Platforms are required for profile management, as a function of experimental design, and for metabolite identification, to facilitate the mining of the corresponding data. Various databases have been created, including organism-specific knowledgebases and analytical technique-specific spectral databases. However, there is currently no platform meeting the requirements for both profile management and metabolite identification for nuclear magnetic resonance (NMR) experiments. Description MeRy-B, the first platform for plant 1H-NMR metabolomic profiles, is designed (i) to provide a knowledgebase of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata, (ii) for queries and visualization of the data, (iii) to discriminate between profiles with spectrum visualization tools and statistical analysis, (iv) to facilitate compound identification. It contains lists of plant metabolites and unknown compounds, with information about experimental conditions, the factors studied and metabolite concentrations for several plant species, compiled from more than one thousand annotated NMR profiles for various organs or tissues. Conclusion MeRy-B manages all the data generated by NMR-based plant metabolomics experiments, from description of the biological source to identification of the metabolites and determinations of their concentrations. It is the first database allowing the display and overlay of NMR metabolomic profiles selected through queries on data or metadata. MeRy-B is available from http://www.cbib.u-bordeaux2.fr/MERYB/index.php. PMID:21668943

Metabolomics discloses donor liver biomarkers associated with early allograft dysfunction.

PubMed

Cortes, Miriam; Pareja, Eugenia; García-Cañaveras, Juan C; Donato, M Teresa; Montero, Sandra; Mir, Jose; Castell, José V; Lahoz, Agustín

2014-09-01

Early allograft dysfunction (EAD) dramatically influences graft and patient outcome after orthotopic liver transplantation and its incidence is strongly determined by donor liver quality. Nevertheless, objective biomarkers, which can assess graft quality and anticipate organ function, are still lacking. This study aims to investigate whether there is a preoperative donor liver metabolomic biosignature associated with EAD. A comprehensive metabolomic profiling of 124 donor liver biopsies collected before transplantation was performed by mass spectrometry coupled to liquid chromatography. Donor liver grafts were classified into two groups: showing EAD and immediate graft function (IGF). Multivariate data analysis was used to search for the relationship between the metabolomic profiles present in donor livers before transplantation and their function in recipients. A set of liver graft dysfunction-associated biomarkers was identified. Key changes include significantly increased levels of bile acids, lysophospholipids, phospholipids, sphingomyelins and histidine metabolism products, all suggestive of disrupted lipid homeostasis and altered histidine pathway. Based on these biomarkers, a predictive EAD model was built and further evaluated by assessing 24 independent donor livers, yielding 91% sensitivity and 82% specificity. The model was also successfully challenged by evaluating donor livers showing primary non-function (n=4). A metabolomic biosignature that accurately differentiates donor livers, which later showed EAD or IGF, has been deciphered. The remarkable metabolomic differences between donor livers before transplant can relate to their different quality. The proposed metabolomic approach may become a clinical tool for donor liver quality assessment and for anticipating graft function before transplant. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Disruption of TCA Cycle and Glutamate Metabolism Identified by Metabolomics in an In Vitro Model of Amyotrophic Lateral Sclerosis.

PubMed

Veyrat-Durebex, Charlotte; Corcia, Philippe; Piver, Eric; Devos, David; Dangoumau, Audrey; Gouel, Flore; Vourc'h, Patrick; Emond, Patrick; Laumonnier, Frédéric; Nadal-Desbarats, Lydie; Gordon, Paul H; Andres, Christian R; Blasco, Hélène

2016-12-01

This study aims to develop a cellular metabolomics model that reproduces the pathophysiological conditions found in amyotrophic lateral sclerosis in order to improve knowledge of disease physiology. We used a co-culture model combining the motor neuron-like cell line NSC-34 and the astrocyte clone C8-D1A, with each over-expressing wild-type or G93C mutant human SOD1, to examine amyotrophic lateral sclerosis (ALS) physiology. We focused on the effects of mutant human SOD1 as well as oxidative stress induced by menadione on intracellular metabolism using a metabolomics approach through gas chromatography coupled with mass spectrometry (GC-MS) analysis. Preliminary non-supervised analysis by Principal Component Analysis (PCA) revealed that cell type, genetic environment, and time of culture influenced the metabolomics profiles. Supervised analysis using orthogonal partial least squares discriminant analysis (OPLS-DA) on data from intracellular metabolomics profiles of SOD1 G93C co-cultures produced metabolites involved in glutamate metabolism and the tricarboxylic acid cycle (TCA) cycle. This study revealed the feasibility of using a metabolomics approach in a cellular model of ALS. We identified potential disruption of the TCA cycle and glutamate metabolism under oxidative stress, which is consistent with prior research in the disease. Analysis of metabolic alterations in an in vitro model is a novel approach to investigation of disease physiology.

Serum metabolomics study of Traditional Chinese medicine formula intervention to polycystic ovary syndrome.

PubMed

Lu, Caixia; Zhao, Xinjie; Li, Yan; Li, Yanjie; Yuan, Chengkun; Xu, Fang; Meng, Xiaoyu; Hou, Lihui; Xu, Guowang

2016-02-20

Polycystic ovary syndrome (PCOS) is a most common, heterogeneous, complex endocrinopathy disease. Traditional Chinese medicine (TCM) has been used in the treatment of PCOS for many years. However, the mechanism underlying TCM remains obscure and challenging. In this study, 30 PCOS subjects were separated into normoinsulinemic group (NI=13) and hyperinsulinemic group (HI=17), and treated for three menstrual cycles with TCM Formula, Bushen Huatan Formula (BHF). A metabolomics approach based on ultra-high-performance liquid chromatography (UPLC) coupled with linear ion trap Orbi-trap mass spectrometer (LTQ Orbi-trap MS) is used to investigate serum metabolic changes of TCM intervention to PCOS. After BHF intervention for three menstrual cycles, the serum levels of glycerophosphorylethanolamine (GPEA), creatine, creatinine decreased in both NI and HI groups. Furthermore, in NI group, the main manifestation was the changes of phospholipid metabolism. While in HI group, lysine, phenol sulfate, phe-phe etc. decreased, and ornithine, proline, betaine, acetylcholine etc. increased. Combined with clinical biochemical data, BHF was proved effective to PCOS by reducing the inflammatory reaction and oxidative stress. This study also illustrates that the LC-MS based metabolomic approach is a helpful tool to evaluate curative effect and to understand the mechanisms of TCM. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrating Milk Metabolite Profile Information for the Prediction of Traditional Milk Traits Based on SNP Information for Holstein Cows

PubMed Central

Melzer, Nina; Wittenburg, Dörte; Repsilber, Dirk

2013-01-01

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype). PMID:23990900

Characterization of proteomic and metabolomic responses to dietary factors and supplements.

PubMed

Astle, John; Ferguson, Jonathan T; German, J Bruce; Harrigan, George G; Kelleher, Neil L; Kodadek, Thomas; Parks, Bryan A; Roth, Michael J; Singletary, Keith W; Wenger, Craig D; Mahady, Gail B

2007-12-01

Over the past decade there has been a renewed interest in research and development of both dietary and nutritional supplements. Significant advancements have been made in the scientific assessment of the quality, safety, and efficacy of these products because of the strong interest in and financial support of these projects. As research in both fields continues to advance, opportunities to use new and innovative research technologies and methodologies, such as proteomics and metabolomics, are critical for the future progress of the science. The purpose of the symposium was to begin the process of communicating new innovative proteomic and metabolomic methodologies that may be applied by researchers in both the nutrition and the natural product communities. This symposium highlighted 2 proteomic approaches, protein fingerprinting in complex mixtures with peptoid microarrays and top-down mass spectrometry for annotation of gene products. Likewise, an overview of the methodologies used in metabolomic profiling of natural products was presented, and an illustration of an integrated metabolomics approach in nutrition research was highlighted.

An Ultrahigh-Performance Liquid Chromatography-Time-of-Flight Mass Spectrometry Metabolomic Approach to Studying the Impact of Moderate Red-Wine Consumption on Urinary Metabolome.

PubMed

Esteban-Fernández, Adelaida; Ibañez, Clara; Simó, Carolina; Bartolomé, Begoña; Moreno-Arribas, M Victoria

2018-04-06

Moderate red-wine consumption has been widely described to exert several benefits in human health. This is mainly due to its unique content of bioactive polyphenols, which suffer several modifications along their pass through the digestive system, including microbial transformation in the colon and phase-II metabolism, until they are finally excreted in urine and feces. To determine the impact of moderate wine consumption in the overall urinary metabolome of healthy volunteers ( n = 41), samples from a red-wine interventional study (250 mL/day, 28 days) were investigated. Urine (24 h) was collected before and after intervention and analyzed by an untargeted ultrahigh-performance liquid chromatography-time-of-flight mass spectrometry metabolomics approach. 94 compounds linked to wine consumption, including specific wine components (tartaric acid), microbial-derived phenolic metabolites (5-(dihydroxyphenyl)-γ-valerolactones and 4-hydroxyl-5-(phenyl)-valeric acids), and endogenous compounds were identified. Also, some relationships between parallel fecal and urinary metabolomes are discussed.

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Metabolomics of cancer cell cultures to assess the effects of dietary phytochemicals.

PubMed

Brasili, Elisa; Filho, Valdir Cechinel

2017-05-03

Cancer is a multi-factorial disease and is a major cause of morbidity and mortality worldwide. Dietary phytochemicals have been used for the treatment of cancer throughout history due to their safety, low toxicity, and general availability. Several studies have been performed to elucidate the effects of dietary phytochemicals on cancer metabolism, and many molecular targets of phytochemicals have been discovered. In spite of remarkable progress, their effects on cancer metabolism have not yet been fully clarified. Recent developments in metabolomics allowed to probe much further the metabolism of cancer, highlighting altered metabolic pathways and offering a new powerful tool to investigate cancer disease. In this review, we discuss the main metabolic alterations of cancer cells and the potentiality of phytochemicals as promising modulators of cancer metabolism. We will focus on the application of nuclear magnetic resonance-based metabolomics on breast and hepatocellular cancer cell lines to evaluate the impact of curcumin and resveratrol on cancer metabolome with the aim to demonstrate the premise of this approach to provide useful information for a better understanding of impact of diet components on cancer disease.

High Aerobic Capacity Mitigates Changes in the Plasma Metabolomic Profile Associated with Aging.

PubMed

Falegan, Oluyemi S; Vogel, Hans J; Hittel, Dustin S; Koch, Lauren G; Britton, Steven L; Hepple, Russ T; Shearer, Jane

2017-02-03

Advancing age is associated with declines in maximal oxygen consumption. Declines in aerobic capacity not only contribute to the aging process but also are an independent risk factor for morbidity, cardiovascular disease, and all-cause mortality. Although statistically convincing, the relationships between aerobic capacity, aging, and disease risk remain largely unresolved. To this end, we employed sensitive, system-based metabolomics approach to determine whether enhanced aerobic capacity could mitigate some of the changes seen in the plasma metabolomic profile associated with aging. Metabolomic profiles of plasma samples obtained from young (13 month) and old (26 month) rats bred for low (LCR) or high (HCR) running capacity using proton nuclear magnetic resonance spectroscopy ( 1 H NMR) were examined. Results demonstrated strong profile separation in old and low aerobic capacity rats, whereas young and high aerobic capacity rat models were less predictive. Significantly differential metabolites between the groups include taurine, acetone, valine, and trimethylamine-N-oxide among other metabolites, specifically citrate, succinate, isovalerate, and proline, were differentially increased in older HCR animals compared with their younger counterparts. When interactions between age and aerobic capacity were examined, results demonstrated that enhanced aerobic capacity could mitigate some but not all age-associated alterations in the metabolomic profile.

Dynamic metabolome profiling reveals significant metabolic changes during grain development of bread wheat (Triticum aestivum L.).

PubMed

Zhen, Shoumin; Dong, Kun; Deng, Xiong; Zhou, Jiaxing; Xu, Xuexin; Han, Caixia; Zhang, Wenying; Xu, Yanhao; Wang, Zhimin; Yan, Yueming

2016-08-01

Metabolites in wheat grains greatly influence nutritional values. Wheat provides proteins, minerals, B-group vitamins and dietary fiber to humans. These metabolites are important to human health. However, the metabolome of the grain during the development of bread wheat has not been studied so far. In this work the first dynamic metabolome of the developing grain of the elite Chinese bread wheat cultivar Zhongmai 175 was analyzed, using non-targeted gas chromatography/mass spectrometry (GC/MS) for metabolite profiling. In total, 74 metabolites were identified over the grain developmental stages. Metabolite-metabolite correlation analysis revealed that the metabolism of amino acids, carbohydrates, organic acids, amines and lipids was interrelated. An integrated metabolic map revealed a distinct regulatory profile. The results provide information that can be used by metabolic engineers and molecular breeders to improve wheat grain quality. The present metabolome approach identified dynamic changes in metabolite levels, and correlations among such levels, in developing seeds. The comprehensive metabolic map may be useful when breeding programs seek to improve grain quality. The work highlights the utility of GC/MS-based metabolomics, in conjunction with univariate and multivariate data analysis, when it is sought to understand metabolic changes in developing seeds. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers.

PubMed

Puchades-Carrasco, Leonor; Palomino-Schätzlein, Martina; Pérez-Rambla, Clara; Pineda-Lucena, Antonio

2016-05-01

Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Modelling short time series in metabolomics: a functional data analysis approach.

PubMed

Montana, Giovanni; Berk, Maurice; Ebbels, Tim

2011-01-01

Metabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two or more experimental conditions (e.g. a control and drug-treated group), thus producing time course data. Models from traditional time series analysis are often unsuitable because, by design, only very few time points are available and there are a high number of missing values. We propose a functional data analysis approach for modelling short time series arising in metabolomic studies which overcomes these obstacles. Our model assumes that each observed time series is a smooth random curve, and we propose a statistical approach for inferring this curve from repeated measurements taken on the experimental units. A test statistic for detecting differences between temporal profiles associated with two experimental conditions is then presented. The methodology has been applied to NMR spectroscopy data collected in a pre-clinical toxicology study.

Understanding Plant Nitrogen Metabolism through Metabolomics and Computational Approaches

PubMed Central

Beatty, Perrin H.; Klein, Matthias S.; Fischer, Jeffrey J.; Lewis, Ian A.; Muench, Douglas G.; Good, Allen G.

2016-01-01

A comprehensive understanding of plant metabolism could provide a direct mechanism for improving nitrogen use efficiency (NUE) in crops. One of the major barriers to achieving this outcome is our poor understanding of the complex metabolic networks, physiological factors, and signaling mechanisms that affect NUE in agricultural settings. However, an exciting collection of computational and experimental approaches has begun to elucidate whole-plant nitrogen usage and provides an avenue for connecting nitrogen-related phenotypes to genes. Herein, we describe how metabolomics, computational models of metabolism, and flux balance analysis have been harnessed to advance our understanding of plant nitrogen metabolism. We introduce a model describing the complex flow of nitrogen through crops in a real-world agricultural setting and describe how experimental metabolomics data, such as isotope labeling rates and analyses of nutrient uptake, can be used to refine these models. In summary, the metabolomics/computational approach offers an exciting mechanism for understanding NUE that may ultimately lead to more effective crop management and engineered plants with higher yields. PMID:27735856

Application of an untargeted metabolomics approach for the identification of compounds that may be responsible for observed differential effects in chickens fed an organic and a conventional diet.

PubMed

Ruiz-Aracama, A; Lommen, A; Huber, M; van de Vijver, L; Hoogenboom, R

2012-01-01

The aim of this study was to apply an untargeted NMR and LC-MS-based metabolomics approach to detect potential differences between an organically and a conventionally produced feed, which caused statistically significant differences in growth, in the response to an immunological challenge and in the gene expression profiles in the small intestine of laying hens. A fractionation procedure was set up to create multiple fractions of the feed, which were subsequently analysed by NMR and UPLC-TOF/MS operating in positive mode. Comparison of the profiles revealed that the most apparent differences came from the isoflavones in the soy as well as a compound with a molecular mass of 441.202 (M + 1)⁺, which was identified as N,N'-diferuloylputrescine (DFP) and came from the corn. Whether the observed differences in effects are due to the higher levels of isoflavones and DFP is unclear, as is the fact whether the observed differences are typical for organic or conventional produced corn and soy. However, this study shows that this metabolomics approach is suitable for detecting potential differences between products, even in levels of compounds that would have been overlooked with a more targeted approach. As such, the method is suitable for a more systematic study on differences between conventionally and organically produced food.

Metabolomic markers of fertility in bull seminal plasma

PubMed Central

Dinh, Thu; Kaya, Abdullah; Topper, Einko; Moura, Arlindo Alencar

2018-01-01

Metabolites play essential roles in biological systems, but detailed identities and significance of the seminal plasma metabolome related to bull fertility are still unknown. The objectives of this study were to determine the comprehensive metabolome of seminal plasma from Holstein bulls and to ascertain the potential of metabolites as biomarkers of bull fertility. The seminal plasma metabolome from 16 Holstein bulls with two fertility rates were determined by gas chromatography-mass spectrometry (GC-MS). Multivariate and univariate analyses of the data were performed, and the pathways associated with the seminal plasma metabolome were identified using bioinformatics approaches. Sixty-three metabolites were identified in the seminal plasma of all bulls. Fructose was the most abundant metabolite in the seminal fluid, followed for citric acid, lactic acid, urea and phosphoric acid. Androstenedione, 4-ketoglucose, D-xylofuranose, 2-oxoglutaric acid and erythronic acid represented the least predominant metabolites. Partial-Least Squares Discriminant Analysis (PLSDA) revealed a distinct separation between high and low fertility bulls. The metabolites with the greatest Variable Importance in Projection score (VIP > 2) were 2-oxoglutaric acid and fructose. Heat-map analysis, based on VIP score, and univariate analysis indicated that 2-oxoglutaric acid was less (P = 0.02); whereas fructose was greater (P = 0.02) in high fertility than in low fertility bulls. The current study is the first to describe the metabolome of bull seminal plasma using GC-MS and presented metabolites such as 2-oxoglutaric acid and fructose as potential biomarkers of bull fertility. PMID:29634739

Metabolomics and malaria biology

PubMed Central

Lakshmanan, Viswanathan; Rhee, Kyu Y.; Daily, Johanna P.

2010-01-01

Metabolomics has ushered in a novel and multi-disciplinary realm in biological research. It has provided researchers with a platform to combine powerful biochemical, statistical, computational, and bioinformatics techniques to delve into the mysteries of biology and disease. The application of metabolomics to study malaria parasites represents a major advance in our approach towards gaining a more comprehensive perspective on parasite biology and disease etiology. This review attempts to highlight some of the important aspects of the field of metabolomics, and its ongoing and potential future applications to malaria research. PMID:20970461

Metabolomics and Its Application to Acute Lung Diseases

PubMed Central

Stringer, Kathleen A.; McKay, Ryan T.; Karnovsky, Alla; Quémerais, Bernadette; Lacy, Paige

2016-01-01

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a “snapshot” in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials. PMID:26973643

CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

PubMed

Krishnamurthy, Krish

2013-12-01

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry.

PubMed

Luo, Ping; Dai, Weidong; Yin, Peiyuan; Zeng, Zhongda; Kong, Hongwei; Zhou, Lina; Wang, Xiaolin; Chen, Shili; Lu, Xin; Xu, Guowang

2015-01-01

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of “Unknown Function”

PubMed Central

Quanbeck, Stephanie M.; Brachova, Libuse; Campbell, Alexis A.; Guan, Xin; Perera, Ann; He, Kun; Rhee, Seung Y.; Bais, Preeti; Dickerson, Julie A.; Dixon, Philip; Wohlgemuth, Gert; Fiehn, Oliver; Barkan, Lenore; Lange, Iris; Lange, B. Markus; Lee, Insuk; Cortes, Diego; Salazar, Carolina; Shuman, Joel; Shulaev, Vladimir; Huhman, David V.; Sumner, Lloyd W.; Roth, Mary R.; Welti, Ruth; Ilarslan, Hilal; Wurtele, Eve S.; Nikolau, Basil J.

2012-01-01

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs. PMID:22645570

[Metabolomics research of medicinal plants].

PubMed

Duan, Li-Xin; Dai, Yun-Tao; Sun, Chao; Chen, Shi-Lin

2016-11-01

Metabolomics is the comprehensively study of chemical processes involving small molecule metabolites. It is an important part of systems biology, and is widely applied in complex traditional Chinese medicine（TCM）system. Metabolites biosynthesized by medicinal plants are the effective basis for TCM. Metabolomics studies of medicinal plants will usher in a new period of vigorous development with the implementation of Herb Genome Program and the development of TCM synthetic biology. This manuscript introduces the recent research progresses of metabolomics technology and the main research contents of metabolomics studies for medicinal plants, including identification and quality evaluation for medicinal plants, cultivars breeding, stress resistance, metabolic pathways, metabolic network, metabolic engineering and synthetic biology researches. The integration of genomics, transcriptomics and metabolomics approaches will finally lay foundation for breeding of medicinal plants, R&D, quality and safety evaluation of innovative drug. Copyright© by the Chinese Pharmaceutical Association.

Potential Impact and Study Considerations of Metabolomics in Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association.

PubMed

Cheng, Susan; Shah, Svati H; Corwin, Elizabeth J; Fiehn, Oliver; Fitzgerald, Robert L; Gerszten, Robert E; Illig, Thomas; Rhee, Eugene P; Srinivas, Pothur R; Wang, Thomas J; Jain, Mohit

2017-04-01

Through the measure of thousands of small-molecule metabolites in diverse biological systems, metabolomics now offers the potential for new insights into the factors that contribute to complex human diseases such as cardiovascular disease. Targeted metabolomics methods have already identified new molecular markers and metabolomic signatures of cardiovascular disease risk (including branched-chain amino acids, select unsaturated lipid species, and trimethylamine- N -oxide), thus in effect linking diverse exposures such as those from dietary intake and the microbiota with cardiometabolic traits. As technologies for metabolomics continue to evolve, the depth and breadth of small-molecule metabolite profiling in complex systems continue to advance rapidly, along with prospects for ongoing discovery. Current challenges facing the field of metabolomics include scaling throughput and technical capacity for metabolomics approaches, bioinformatic and chemoinformatic tools for handling large-scale metabolomics data, methods for elucidating the biochemical structure and function of novel metabolites, and strategies for determining the true clinical relevance of metabolites observed in association with cardiovascular disease outcomes. Progress made in addressing these challenges will allow metabolomics the potential to substantially affect diagnostics and therapeutics in cardiovascular medicine. © 2017 American Heart Association, Inc.

Discovery of Antimalarial Drugs from Streptomycetes Metabolites Using a Metabolomic Approach

PubMed Central

Baba, Mohd Shukri

2017-01-01

Natural products continue to play an important role as a source of biologically active substances for the development of new drug. Streptomyces, Gram-positive bacteria which are widely distributed in nature, are one of the most popular sources of natural antibiotics. Recently, by using a bioassay-guided fractionation, an antimalarial compound, Gancidin-W, has been discovered from these bacteria. However, this classical method in identifying potentially novel bioactive compounds from the natural products requires considerable effort and is a time-consuming process. Metabolomics is an emerging “omics” technology in systems biology study which integrated in process of discovering drug from natural products. Metabolomics approach in finding novel therapeutics agent for malaria offers dereplication step in screening phase to shorten the process. The highly sensitive instruments, such as Liquid Chromatography-Mass Spectrophotometry (LC-MS), Gas Chromatography-Mass Spectrophotometry (GC-MS), and Nuclear Magnetic Resonance (1H-NMR) spectroscopy, provide a wide range of information in the identification of potentially bioactive compounds. The current paper reviews concepts of metabolomics and its application in drug discovery of malaria treatment as well as assessing the antimalarial activity from natural products. Metabolomics approach in malaria drug discovery is still new and needs to be initiated, especially for drug research in Malaysia. PMID:29123551

Profile-Based LC-MS Data Alignment—A Bayesian Approach

PubMed Central

Tsai, Tsung-Heng; Tadesse, Mahlet G.; Wang, Yue; Ressom, Habtom W.

2014-01-01

A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets. PMID:23929872

Can Untargeted Metabolomics Be Utilized in Drug Discovery/Development?

PubMed

Caldwell, Gary W; Leo, Gregory C

2017-01-01

Untargeted metabolomics is a promising approach for reducing the significant attrition rate for discovering and developing drugs in the pharmaceutical industry. This review aims to highlight the practical decision-making value of untargeted metabolomics for the advancement of drug candidates in drug discovery/development including potentially identifying and validating novel therapeutic targets, creating alternative screening paradigms, facilitating the selection of specific and translational metabolite biomarkers, identifying metabolite signatures for the drug efficacy mechanism of action, and understanding potential drug-induced toxicity. The review provides an overview of the pharmaceutical process workflow to discover and develop new small molecule drugs followed by the metabolomics process workflow that is involved in conducting metabolomics studies. The pros and cons of the major components of the pharmaceutical and metabolomics workflows are reviewed and discussed. Finally, selected untargeted metabolomics literature examples, from primarily 2010 to 2016, are used to illustrate why, how, and where untargeted metabolomics can be integrated into the drug discovery/preclinical drug development process. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Metabolomics in amyotrophic lateral sclerosis: how far can it take us?

PubMed

Blasco, H; Patin, F; Madji Hounoum, B; Gordon, P H; Vourc'h, P; Andres, C R; Corcia, P

2016-03-01

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. © 2016 EAN.

SELDI PROTEINCHIP-BASED LIVER BIOMARKERS IN FUNGICIDE EXPOSED ZEBRAFISH

EPA Science Inventory

The research presented here is part of a three-phased small fish computational toxicology project using a combination of 1) whole organism endpoints, 2) genomic, proteomic, and metabolomic approaches, and 3) computational modeling to (a) identify new molecular biomarkers of expos...

Influential Parameters for the Analysis of Intracellular Parasite Metabolomics.

PubMed

Carey, Maureen A; Covelli, Vincent; Brown, Audrey; Medlock, Gregory L; Haaren, Mareike; Cooper, Jessica G; Papin, Jason A; Guler, Jennifer L

2018-04-25

Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum , both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro -grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite's extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms. IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite's response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens. Copyright © 2018 Carey et al.

Untargeted metabolomics reveals specific withanolides and fatty acyl glycoside as tentative metabolites to differentiate organic and conventional Physalis peruviana fruits.

PubMed

Llano, Sandra M; Muñoz-Jiménez, Ana M; Jiménez-Cartagena, Claudio; Londoño-Londoño, Julián; Medina, Sonia

2018-04-01

The agronomic production systems may affect the levels of food metabolites. Metabolomics approaches have been applied as useful tool for the characterization of fruit metabolome. In this study, metabolomics techniques were used to assess the differences in phytochemical composition between goldenberry samples produced by organic and conventional systems. To verify that the organic samples were free of pesticides, individual pesticides were analyzed. Principal component analysis showed a clear separation of goldenberry samples from two different farming systems. Via targeted metabolomics assays, whereby carotenoids and ascorbic acid were analyzed, not statistical differences between both crops were found. Conversely, untargeted metabolomics allowed us to identify two withanolides and one fatty acyl glycoside as tentative metabolites to differentiate goldenberry fruits, recording organic fruits higher amounts of these compounds than conventional samples. Hence, untargeted metabolomics technology could be suitable to research differences on phytochemicals under different agricultural management practices and to authenticate organic products. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of NMR-based metabolomics to the study of gut microbiota in obesity.

PubMed

Calvani, Riccardo; Brasili, Elisa; Praticò, Giulia; Sciubba, Fabio; Roselli, Marianna; Finamore, Alberto; Marini, Federico; Marzetti, Emanuele; Miccheli, Alfredo

2014-01-01

Lifestyle habits, host gene repertoire, and alterations in the intestinal microbiota concur to the development of obesity. A great deal of research has recently been focused on investigating the role gut microbiota plays in the pathogenesis of metabolic dysfunctions and increased adiposity. Altered microbiota can affect host physiology through several pathways, including enhanced energy harvest, and perturbations in immunity, metabolic signaling, and inflammatory pathways. A broad range of "omics" technologies is now available to help decipher the interactions between the host and the gut microbiota at detailed genetic and functional levels. In particular, metabolomics--the comprehensive analysis of metabolite composition of biological fluids and tissues--could provide breakthrough insights into the links among the gut microbiota, host genetic repertoire, and diet during the development and progression of obesity. Here, we briefly review the most insightful findings on the involvement of gut microbiota in the pathogenesis of obesity. We also discuss how metabolomic approaches based on nuclear magnetic resonance spectroscopy could help understand the activity of gut microbiota in relation to obesity, and assess the effects of gut microbiota modulation in the treatment of this condition.

Untargeted metabolomics of colonic digests reveals kynurenine pathway metabolites, dityrosine and 3-dehydroxycarnitine as red versus white meat discriminating metabolites

PubMed Central

Rombouts, Caroline; Hemeryck, Lieselot Y.; Van Hecke, Thomas; De Smet, Stefaan; De Vos, Winnok H.; Vanhaecke, Lynn

2017-01-01

Epidemiological research has demonstrated that the consumption of red meat is an important risk factor for the development of colorectal cancer (CRC), diabetes mellitus and cardiovascular diseases. However, there is no holistic insight in the (by-) products of meat digestion that may contribute to disease development. To address this hiatus, an untargeted mass spectrometry (MS)-based metabolomics approach was used to create red versus white meat associated metabolic fingerprints following in vitro colonic digestion using the fecal inocula of ten healthy volunteers. Twenty-two metabolites were unequivocally associated with simulated colonic digestion of red meat. Several of these metabolites could mechanistically be linked to red meat-associated pathways including N’-formylkynurenine, kynurenine and kynurenic acid (all involved in tryptophan metabolism), the oxidative stress marker dityrosine, and 3-dehydroxycarnitine. In conclusion, the used MS-based metabolomics platform proved to be a powerful platform for detection of specific metabolites that improve the understanding of the causal relationship between red meat consumption and associated diseases. PMID:28195169

A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: A case study of Panax ginseng: Metabolomics for the authenticity of herbal medicine.

PubMed

Nguyen, Huy Truong; Lee, Dong-Kyu; Choi, Young-Geun; Min, Jung-Eun; Yoon, Sang Jun; Yu, Yun-Hyun; Lim, Johan; Lee, Jeongmi; Kwon, Sung Won; Park, Jeong Hill

2016-05-30

Ginseng, the root of Panax ginseng has long been the subject of adulteration, especially regarding its origins. Here, 60 ginseng samples from Korea and China initially displayed similar genetic makeup when investigated by DNA-based technique with 23 chloroplast intergenic space regions. Hence, (1)H NMR-based metabolomics with orthogonal projections on the latent structure-discrimination analysis (OPLS-DA) were applied and successfully distinguished between samples from two countries using seven primary metabolites as discrimination markers. Furthermore, to recreate adulteration in reality, 21 mixed samples of numerous Korea/China ratios were tested with the newly built OPLS-DA model. The results showed satisfactory separation according to the proportion of mixing. Finally, a procedure for assessing mixing proportion of intentionally blended samples that achieved good predictability (adjusted R(2)=0.8343) was constructed, thus verifying its promising application to quality control of herbal foods by pointing out the possible mixing ratio of falsified samples. Copyright © 2016 Elsevier B.V. All rights reserved.

(1)H NMR-Based Metabolomics and Neurotoxicity Study of Cerebrum and Cerebellum in Rats Treated with Cinnabar, a Traditional Chinese Medicine.

PubMed

Wei, Lai; Xue, Rong; Zhang, Panpan; Wu, Yijie; Li, Xiaojing; Pei, Fengkui

2015-08-01

Cinnabar, an important traditional Chinese mineral medicine, has been widely used as a Chinese patent medicine ingredient for sedative therapy. Nevertheless, the neurotoxic effects of cinnabar have also been noted. In this study, (1)H NMR-based metabolomics, combined with multivariate pattern recognition, were applied to investigate the neurotoxic effects of cinnabar after intragastrical administration (dosed at 2 and 5 g/kg body weight) on male Wistar rats. The metabolite variations induced by cinnabar were characterized by increased levels of glutamate, glutamine, myo-inositol, and choline, as well as decreased levels of GABA, taurine, NAA, and NAAG in tissue extracts of the cerebellum and cerebrum. These findings suggested that cinnabar induced glutamate excitotoxicity, neuronal cell loss, osmotic state changes, membrane fluidity disruption, and oxidative injury in the brain. We also show here that there is a dose- and time-dependent neurotoxicity of cinnabar, and that cerebellum was more sensitive to cinnabar induction than cerebrum. This work illustrates the utility and reliability of (1)H NMR-based metabolomics approach for examining the potential neurotoxic effects of cinnabar and other traditional Chinese medicines.

Use of elemental and molecular-mass spectrometry to assess the toxicological effects of inorganic mercury in the mouse Mus musculus.

PubMed

García-Sevillano, Miguel Angel; García-Barrera, Tamara; Navarro, Francisco; Gailer, Jürgen; Gómez-Ariza, José Luiz

2014-09-01

The biochemical response of mice (Mus musculus) to acute subcutaneous inorganic-mercury exposure was assessed over a 14-day period by analyzing cytosolic extracts of the liver, the kidneys, and blood plasma. Integrated metallomic and metabolomic approaches using elemental and molecular-mass spectrometry were used to obtain comprehensive insight into the toxicological effects of mercury regarding its distribution and possible perturbation of metabolic pathways. The metallomic approach involved the use of size-exclusion chromatography (SEC) coupled with multiaffinity chromatography inductively coupled plasma-mass spectrometry (ICP-MS) and isotopic-dilution analysis. The metabolomic approach involved the direct infusion of polar and lipophilic tissue extracts into a mass spectrometer (DIMS) in the positive and negative acquisition mode (ESI+and ESI-). The use of SEC-ICP-MS enabled us to detect changes in the metalloproteome in the liver and the kidneys during the exposure period, and revealed that interactions between Hg and endogenous Cu and Zn adversely affected the homeostasis of these essential metals. The detection of an Hg-Se detoxification product in mouse plasma substantiated the known interaction between Hg and Se in mammals. Use of DIMS in conjunction with partial-least-squares discriminant analysis (PLS-DA) uncovered time-dependent changes of endogenous metabolites over time, corroborated by histopathology investigation of specific mouse tissues. The perturbations of endogenous metabolic profiles were explained in terms of the adverse effect of mercury on energy metabolism (e.g. glycolysis, Krebs cycle), the degradation of membrane phospholipids (apoptosis), and increased levels of specific lipids in plasma. In summary, use of an SEC-ICP-MS-based metallomics approach in conjunction with molecular-mass-spectrometry-based metabolomics is revealed as a promising strategy to more comprehensively investigate the toxicological effects of harmful environmental pollutants and xenobiotics.

Metabolic fate of polyphenols in the human superorganism

PubMed Central

van Duynhoven, John; Vaughan, Elaine E.; Jacobs, Doris M.; Kemperman, Robèr A.; van Velzen, Ewoud J. J.; Gross, Gabriele; Roger, Laure C.; Possemiers, Sam; Smilde, Age K.; Doré, Joël; Westerhuis, Johan A.; Van de Wiele, Tom

2011-01-01

Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations. PMID:20615997

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access.

PubMed

Salek, Reza M; Neumann, Steffen; Schober, Daniel; Hummel, Jan; Billiau, Kenny; Kopka, Joachim; Correa, Elon; Reijmers, Theo; Rosato, Antonio; Tenori, Leonardo; Turano, Paola; Marin, Silvia; Deborde, Catherine; Jacob, Daniel; Rolin, Dominique; Dartigues, Benjamin; Conesa, Pablo; Haug, Kenneth; Rocca-Serra, Philippe; O'Hagan, Steve; Hao, Jie; van Vliet, Michael; Sysi-Aho, Marko; Ludwig, Christian; Bouwman, Jildau; Cascante, Marta; Ebbels, Timothy; Griffin, Julian L; Moing, Annick; Nikolski, Macha; Oresic, Matej; Sansone, Susanna-Assunta; Viant, Mark R; Goodacre, Royston; Günther, Ulrich L; Hankemeier, Thomas; Luchinat, Claudio; Walther, Dirk; Steinbeck, Christoph

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

Simultaneous measurement of NAD metabolome in aged mice tissue using liquid chromatography tandem-mass spectrometry.

PubMed

Yaku, Keisuke; Okabe, Keisuke; Nakagawa, Takashi

2018-06-01

Nicotinamide adenine dinucleotide (NAD) is a major co-factor that mediates multiple biological processes including redox reaction and gene expression. Recently, NAD metabolism has received considerable attention because administration of NAD precursors exhibited beneficial effects against aging-related metabolic disorders in animals. Although numerous studies have reported that NAD levels decline with aging in multiple animal tissues, the pathway and kinetics of NAD metabolism in aged organs are not completely understood. To determine the NAD metabolism upon aging, we developed targeted metabolomics based on an LC/MS/MS system. Our method is simple and applicable to crude biological samples, including culture cells and animal tissues. Unlike a conventional enzymatic cycling assay, our approach can determine NAD and NADH (reduced form of NAD) by performing a single sample preparation. Further, we validated our method using biological samples and investigated the alteration of the NAD metabolome during aging. Consistent with previous reports, the NAD levels in the liver and skeletal muscle decreased with aging. Further, we detected a significant increase in nicotinamide mononucleotide and nicotinamide riboside in the kidney upon aging. The LC/MS/MS-based NAD metabolomics that we have developed is extensively applicable to biomedical studies, and the results will present innovative ideas for the aging studies, especially for that of NAD metabolism. Copyright © 2018 John Wiley & Sons, Ltd.

UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption.

PubMed

Liu, Haiyan; Garrett, Timothy J; Su, Zhihua; Khoo, Christina; Gu, Liwei

2017-07-01

Plasma metabolome in young women following cranberry juice consumption were investigated using a global UHPLC-Q-Orbitrap-HRMS approach. Seventeen female college students, between 21 and 29 years old, were given either cranberry juice or apple juice for three days using a cross-over design. Plasma samples were collected before and after juice consumption. Plasma metabolomes were analyzed using UHPLC-Q-Orbitrap-HRMS followed by orthogonal partial least squares-discriminant analyses (OPLS-DA). S-plot was used to identify discriminant metabolites. Validated OPLS-DA analyses showed that the plasma metabolome in young women, including both exogenous and endogenous metabolites, were altered following cranberry juice consumption. Cranberry juice caused increases of exogenous metabolites including quinic acid, vanilloloside, catechol sulfate, 3,4-dihydroxyphenyl ethanol sulfate, coumaric acid sulfate, ferulic acid sulfate, 5-(trihydroxphenyl)-gamma-valerolactone, 3-(hydroxyphenyl)proponic acid, hydroxyphenylacetic acid and trihydroxybenzoic acid. In addition, the plasma levels of endogenous metabolites including citramalic acid, aconitic acid, hydroxyoctadecanoic acid, hippuric acid, 2-hydroxyhippuric acid, vanilloylglycine, 4-acetamido-2-aminobutanoic acid, dihydroxyquinoline, and glycerol 3-phosphate were increased in women following cranberry juice consumption. The metabolic differences and discriminant metabolites observed in this study may serve as biomarkers of cranberry juice consumption and explain its health promoting properties in human. Copyright © 2017 Elsevier Inc. All rights reserved.

Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe.

PubMed

Lamichhane, Santosh; Sen, Partho; Dickens, Alex M; Orešič, Matej; Bertram, Hanne Christine

2018-04-30

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted. Copyright © 2018 Elsevier Inc. All rights reserved.

Exogenous alanine and/or glucose plus kanamycin kills antibiotic-resistant bacteria.

PubMed

Peng, Bo; Su, Yu-Bin; Li, Hui; Han, Yi; Guo, Chang; Tian, Yao-Mei; Peng, Xuan-Xian

2015-02-03

Multidrug-resistant bacteria are an increasingly serious threat to human and animal health. However, novel drugs that can manage infections by multidrug-resistant bacteria have proved elusive. Here we show that glucose and alanine abundances are greatly suppressed in kanamycin-resistant Edwardsiella tarda by GC-MS-based metabolomics. Exogenous alanine or glucose restores susceptibility of multidrug-resistant E. tarda to killing by kanamycin, demonstrating an approach to killing multidrug-resistant bacteria. The mechanism underlying this approach is that exogenous glucose or alanine promotes the TCA cycle by substrate activation, which in turn increases production of NADH and proton motive force and stimulates uptake of antibiotic. Similar results are obtained with other Gram-negative bacteria (Vibrio parahaemolyticus, Klebsiella pneumoniae, Pseudomonas aeruginosa) and Gram-positive bacterium (Staphylococcus aureus), and the results are also reproduced in a mouse model for urinary tract infection. This study establishes a functional metabolomics-based strategy to manage infection by antibiotic-resistant bacteria. Copyright © 2015 Elsevier Inc. All rights reserved.

Comprehensive profiling and marker identification in non-volatile citrus oil residues by mass spectrometry and nuclear magnetic resonance.

PubMed

Marti, Guillaume; Boccard, Julien; Mehl, Florence; Debrus, Benjamin; Marcourt, Laurence; Merle, Philippe; Delort, Estelle; Baroux, Lucie; Sommer, Horst; Rudaz, Serge; Wolfender, Jean-Luc

2014-05-01

The detailed characterization of cold-pressed lemon oils (CPLOs) is of great importance for the flavor and fragrance (F&F) industry. Since a control of authenticity by standard analytical techniques can be bypassed using elaborated adulterated oils to pretend a higher quality, a combination of advanced orthogonal methods has been developed. The present study describes a combined metabolomic approach based on UHPLC-TOF-MS profiling and (1)H NMR fingerprinting to highlight metabolite differences on a set of representative samples used in the F&F industry. A new protocol was set up and adapted to the use of CPLO residues. Multivariate analysis based on both fingerprinting methods showed significant chemical variations between Argentinian and Italian samples. Discriminating markers identified in mixtures belong to furocoumarins, flavonoids, terpenoids and fatty acids. Quantitative NMR revealed low citropten and high bergamottin content in Italian samples. The developed metabolomic approach applied to CPLO residues gives some new perspectives for authenticity assessment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Monitoring Ecological Impacts of Environmental Surface Waters using Cell-based Metabolomics

EPA Science Inventory

Optimized cell-based metabolomics has been used to study the impacts of contaminants in surface waters on human and fish metabolomes. This method has proven to be resource- and time-effective, as well as sustainable for long term and large scale studies. In the current study, cel...

Using NMR-based metabolomics to monitor the biochemical composition of agricultural soils: a pilot study

USDA-ARS?s Scientific Manuscript database

NMR-based metabolomics plays a major role studying complex living systems. However, very few studies describe the application of this technique to the evaluation of soil metabolome. Here, we introduce a protocol for analyzing the biochemical compounds from agricultural soils where the microbial comm...

Role of metabolomics in TBI research

PubMed Central

Wolahan, Stephanie M.; Hirt, Daniel; Braas, Daniel; Glenn, Thomas C.

2016-01-01

Synopsis Metabolomics is an important member of the omics community in that it defines which small molecules may be responsible for disease states. This article reviews the essential principles of metabolomics from specimen preparation, chemical analysis, and advanced statistical methods. Metabolomics in TBI has so far been underutilized. Future metabolomics based studies focused on the diagnoses, prognoses, and treatment effects, need to be conducted across all types of TBI. PMID:27637396

ROMANCE: A new software tool to improve data robustness and feature identification in CE-MS metabolomics.

PubMed

González-Ruiz, Víctor; Gagnebin, Yoric; Drouin, Nicolas; Codesido, Santiago; Rudaz, Serge; Schappler, Julie

2018-05-01

The use of capillary electrophoresis coupled to mass spectrometry (CE-MS) in metabolomics remains an oddity compared to the widely adopted use of liquid chromatography. This technique is traditionally regarded as lacking the reproducibility to adequately identify metabolites by their migration times. The major reason is the variability of the velocity of the background electrolyte, mainly coming from shifts in the magnitude of the electroosmotic flow and from the suction caused by electrospray interfaces. The use of the effective electrophoretic mobility is one solution to overcome this issue as it is a characteristic feature of each compound. To date, such an approach has not been applied to metabolomics due to the complexity and size of CE-MS data obtained in such studies. In this paper, ROMANCE (RObust Metabolomic Analysis with Normalized CE) is introduced as a new software for CE-MS-based metabolomics. It allows the automated conversion of batches of CE-MS files with minimal user intervention. ROMANCE converts the x-axis of each MS file from the time into the effective mobility scale and the resulting files are already pseudo-aligned, present normalized peak areas and improved reproducibility, and can eventually follow existing metabolomic workflows. The software was developed in Scala, so it is multi-platform and computationally-efficient. It is available for download under a CC license. In this work, the versatility of ROMANCE was demonstrated by using data obtained in the same and in different laboratories, as well as its application to the analysis of human plasma samples. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metabolomic mapping of cancer stem cells for reducing and exploiting tumor heterogeneity.

PubMed

Cuyàs, Elisabet; Verdura, Sara; Fernández-Arroyo, Salvador; Bosch-Barrera, Joaquim; Martin-Castillo, Begoña; Joven, Jorge; Menendez, Javier A

2017-11-21

Personalized cancer medicine based on the analysis of tumors en masse is limited by tumor heterogeneity, which has become a major obstacle to effective cancer treatment. Cancer stem cells (CSC) are emerging as key drivers of inter- and intratumoral heterogeneity. CSC have unique metabolic dependencies that are required not only for specific bioenergetic/biosynthetic demands but also for sustaining their operational epigenetic traits, i.e. self-renewal, tumor-initiation, and plasticity. Given that the metabolome is the final downstream product of all the -omic layers and, therefore, most representative of the biological phenotype, we here propose that a novel approach to better understand the complexity of tumor heterogeneity is by mapping and cataloging small numbers of CSC metabolomic phenotypes. The narrower metabolomic diversity of CSC states could be employed to reduce multidimensional tumor heterogeneity into dynamic models of fewer actionable sub-phenotypes. The identification of the driver nodes that are used differentially by CSC states to metabolically regulate self-renewal and tumor initation and escape chemotherapy might open new preventive and therapeutic avenues. The mapping of CSC metabolomic states could become a pioneering strategy to reduce the dimensionality of tumor heterogeneity and improve our ability to examine changes in tumor cell populations for cancer detection, prognosis, prediction/monitoring of therapy response, and detection of therapy resistance and recurrent disease. The identification of driver metabolites and metabolic nodes accounting for a large amount of variance within the CSC metabolomic sub-phenotypes might offer new unforeseen opportunities for reducing and exploiting tumor heterogeneity via metabolic targeting of CSC.

The Chemistry of Plant–Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

PubMed Central

Mhlongo, Msizi I.; Piater, Lizelle A.; Madala, Ntakadzeni E.; Labuschagne, Nico; Dubery, Ian A.

2018-01-01

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant–plant and plant–microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground ‘signalomics’, and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial – and priming events. PMID:29479360

Metabolic Profiling as a Screening Tool for Cytotoxic Compounds: Identification of 3-Alkyl Pyridine Alkaloids from Sponges Collected at a Shallow Water Hydrothermal Vent Site North of Iceland

PubMed Central

Einarsdottir, Eydis; Magnusdottir, Manuela; Astarita, Giuseppe; Köck, Matthias; Ögmundsdottir, Helga M.; Thorsteinsdottir, Margret; Rapp, Hans Tore; Omarsdottir, Sesselja; Paglia, Giuseppe

2017-01-01

Twenty-eight sponge specimens were collected at a shallow water hydrothermal vent site north of Iceland. Extracts were prepared and tested in vitro for cytotoxic activity, and eight of them were shown to be cytotoxic. A mass spectrometry (MS)-based metabolomics approach was used to determine the chemical composition of the extracts. This analysis highlighted clear differences in the metabolomes of three sponge specimens, and all of them were identified as Haliclona (Rhizoniera) rosea (Bowerbank, 1866). Therefore, these specimens were selected for further investigation. Haliclona rosea metabolomes contained a class of potential key compounds, the 3-alkyl pyridine alkaloids (3-APA) responsible for the cytotoxic activity of the fractions. Several 3-APA compounds were tentatively identified including haliclamines, cyclostellettamines, viscosalines and viscosamines. Among these compounds, cyclostellettamine P was tentatively identified for the first time by using ion mobility MS in time-aligned parallel (TAP) fragmentation mode. In this work, we show the potential of applying metabolomics strategies and in particular the utility of coupling ion mobility with MS for the molecular characterization of sponge specimens. PMID:28241423

Application of Stable Isotope-Assisted Metabolomics for Cell Metabolism Studies

PubMed Central

You, Le; Zhang, Baichen; Tang, Yinjie J.

2014-01-01

The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other “omics” analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research. PMID:24957020

Metabolomic Approaches for Characterizing Aquatic Ecosystems

EPA Science Inventory

Metabolomics is becoming a well-established tool for studying how organisms, such as fish, respond to various stressors. For example, the literature is rich with laboratory studies involving analysis of samples from organisms exposed to individual chemical toxicants. These studie...

Chemomics-based marker compounds mining and mimetic processing for exploring chemical mechanisms in traditional processing of herbal medicines, a continuous study on Rehmanniae Radix.

PubMed

Zhou, Li; Xu, Jin-Di; Zhou, Shan-Shan; Shen, Hong; Mao, Qian; Kong, Ming; Zou, Ye-Ting; Xu, Ya-Yun; Xu, Jun; Li, Song-Lin

2017-12-29

Exploring processing chemistry, in particular the chemical transformation mechanisms involved, is a key step to elucidate the scientific basis in traditional processing of herbal medicines. Previously, taking Rehmanniae Radix (RR) as a case study, the holistic chemome (secondary metabolome and glycome) difference between raw and processed RR was revealed by integrating hyphenated chromatographic techniques-based targeted glycomics and untargeted metabolomics. Nevertheless, the complex chemical transformation mechanisms underpinning the holistic chemome variation in RR processing remain to be extensively clarified. As a continuous study, here a novel strategy by combining chemomics-based marker compounds mining and mimetic processing is proposed for further exploring the chemical mechanisms involved in herbal processing. First, the differential marker compounds between raw and processed herbs were rapidly discovered by untargeted chemomics-based mining approach through multivariate statistical analysis of the chemome data obtained by integrated metabolomics and glycomics analysis. Second, the marker compounds were mimetically processed under the simulated physicochemical conditions as in the herb processing, and the final reaction products were chemically characterized by targeted chemomics-based mining approach. Third, the main chemical transformation mechanisms involved were clarified by linking up the original marker compounds and their mimetic processing products. Using this strategy, a set of differential marker compounds including saccharides, glycosides and furfurals in raw and processed RR was rapidly found, and the major chemical mechanisms involved in RR processing were elucidated as stepwise transformations of saccharides (polysaccharides, oligosaccharides and monosaccharides) and glycosides (iridoid glycosides and phenethylalcohol glycosides) into furfurals (glycosylated/non-glycosylated hydroxymethylfurfurals) by deglycosylation and/or dehydration. The research deliverables indicated that the proposed strategy could advance the understanding of RR processing chemistry, and therefore may be considered a promising approach for delving into the scientific basis in traditional processing of herbal medicines. Copyright © 2017 Elsevier B.V. All rights reserved.

Time is ripe: maturation of metabolomics in chronobiology.

PubMed

Rhoades, Seth D; Sengupta, Arjun; Weljie, Aalim M

2017-02-01

Sleep and circadian rhythms studies have recently benefited from metabolomics analyses, uncovering new connections between chronobiology and metabolism. From untargeted mass spectrometry to quantitative nuclear magnetic resonance spectroscopy, a diversity of analytical approaches has been applied for biomarker discovery in the field. In this review we consider advances in the application of metabolomics technologies which have uncovered significant effects of sleep and circadian cycles on several metabolites, namely phosphatidylcholine species, medium-chain carnitines, and aromatic amino acids. Study design and data processing measures essential for detecting rhythmicity in metabolomics data are also discussed. Future developments in these technologies are anticipated vis-à-vis validating early findings, given metabolomics has only recently entered the ring with other systems biology assessments in chronometabolism studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Toxicity Assessment of 17 alpha-ethinylestradiol by Cell-Culture Based NMR Metabolomics

EPA Science Inventory

A zebrafish liver cell line (ZFL) established from adult zebrafish has been used in a variety of biological research, including toxicology, pharmacology, developmental biology and molecular genetics. The goal of this study is to develop an in vitro approach to identify the respo...

A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

PubMed

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J; Wurtele, Eve Syrkin

2013-04-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery†

PubMed Central

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J.

2013-01-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these dataset with transcriptomic data to create hypotheses concerning specialized metabolism that generates the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software. PMID:23447050

Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High Performance Liquid Chromatography-Mass Spectrometry-based Experimental and Computational Pipeline*

PubMed Central

Ranninger, Christina; Rurik, Marc; Limonciel, Alice; Ruzek, Silke; Reischl, Roland; Wilmes, Anja; Jennings, Paul; Hewitt, Philip; Dekant, Wolfgang; Kohlbacher, Oliver; Huber, Christian G.

2015-01-01

Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 and 35 μmol·liter−1 of chloroacetaldehyde, a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-high resolution mass spectrometry data. Automated data analysis workflows based on open source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration-dependent responses were clearly evident in the metabolomic profiles. To obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative modes, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis, and statistically significant differences were identified using linear models for microarray assays. The analysis revealed toxic effects only for the treatment with 35 μmol·liter−1 for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, among other things, an activation of the Nrf2 and ATF4 pathways. PMID:26055719

Metabolomics and proteomics technologies to explore the herbal preparation affecting metabolic disorders using high resolution mass spectrometry.

PubMed

Zhang, Aihua; Zhou, Xiaohang; Zhao, Hongwei; Zou, Shiyu; Ma, Chung Wah; Liu, Qi; Sun, Hui; Liu, Liang; Wang, Xijun

2017-01-31

An integrative metabolomics and proteomics approach can provide novel insights in the understanding of biological systems. We have integrated proteome and metabolome data sets for a holistic view of the molecular mechanisms in disease. Using quantitative iTRAQ-LC-MS/MS proteomics coupled with UPLC-Q-TOF-HDMS based metabolomics, we determined the protein and metabolite expression changes in the kidney-yang deficiency syndrome (KYDS) rat model and further investigated the intervention effects of the Jinkui Shenqi Pill (JSP). The VIP-plot of the orthogonal PLS-DA (OPLS-DA) was used for discovering the potential biomarkers to clarify the therapeutic mechanisms of JSP in treating KYDS. The results showed that JSP can alleviate the kidney impairment induced by KYDS. Sixty potential biomarkers, including 5-l-glutamyl-taurine, phenylacetaldehyde, 4,6-dihydroxyquinoline, and xanthurenic acid etc., were definitely up- or down-regulated. The regulatory effect of JSP on the disturbed metabolic pathways was proved by the established metabonomic method. Using pathway analyses, we identified the disturbed metabolic pathways such as taurine and hypotaurine metabolism, pyrimidine metabolism, tyrosine metabolism, tryptophan metabolism, histidine metabolism, steroid hormone biosynthesis, etc. Furthermore, using iTRAQ-based quantitative proteomics analysis, seventeen differential proteins were identified and significantly altered by the JSP treatment. These proteins appear to be involved in Wnt, chemokine, PPAR, and MAPK signaling pathways, etc. Functional pathway analysis revealed that most of the proteins were found to play a key role in the regulation of metabolism pathways. Bioinformatics analysis with the IPA software found that these differentially-expressed moleculars had a strong correlation with the α-adrenergic signaling, FGF signaling, etc. Our data indicate that high-throughput metabolomics and proteomics can provide an insight on the herbal preparations affecting the metabolic disorders using high resolution mass spectrometry.

Metabolomics for Plant Improvement: Status and Prospects

PubMed Central

Kumar, Rakesh; Bohra, Abhishek; Pandey, Arun K.; Pandey, Manish K.; Kumar, Anirudh

2017-01-01

Post-genomics era has witnessed the development of cutting-edge technologies that have offered cost-efficient and high-throughput ways for molecular characterization of the function of a cell or organism. Large-scale metabolite profiling assays have allowed researchers to access the global data sets of metabolites and the corresponding metabolic pathways in an unprecedented way. Recent efforts in metabolomics have been directed to improve the quality along with a major focus on yield related traits. Importantly, an integration of metabolomics with other approaches such as quantitative genetics, transcriptomics and genetic modification has established its immense relevance to plant improvement. An effective combination of these modern approaches guides researchers to pinpoint the functional gene(s) and the characterization of massive metabolites, in order to prioritize the candidate genes for downstream analyses and ultimately, offering trait specific markers to improve commercially important traits. This in turn will improve the ability of a plant breeder by allowing him to make more informed decisions. Given this, the present review captures the significant leads gained in the past decade in the field of plant metabolomics accompanied by a brief discussion on the current contribution and the future scope of metabolomics to accelerate plant improvement. PMID:28824660

Metabolomics approach to reduce the Crabtree effect in continuous culture of Saccharomyces cerevisiae.

PubMed

Imura, Makoto; Iwakiri, Ryo; Bamba, Takeshi; Fukusaki, Eiichiro

2018-04-20

The budding yeast Saccharomyces cerevisiae is an important microorganism for fermentation and the food industry. However, during production, S. cerevisiae commonly uses the ethanol fermentation pathway for glucose utilization if excess sugar is present, even in the presence of sufficient oxygen levels. This aerobic ethanol fermentation, referred to as "the Crabtree effect" is one of the most significant reasons for low cell yield. To weaken the Crabtree effect in fed-batch and continuous culture, sugar flow should be limited. In addition, in continuous culture, the dilution rate must be reduced to avoid washing out cells. However, under such conditions, production speed might be sacrificed. It is difficult to solve this problem with the tradeoff between cell yield and production speed by using conventional tactics. However, a metabolomics approach may be an effective way to search for clues regarding metabolic modulation. Therefore, the purpose of this study was to reduce ethanol production in continuous culture of S. cerevisiae at a higher dilution rate through a metabolomics approach. We used a metabolomics analysis to identify metabolites that were drastically increased or decreased in continuous culture when the dilution rate shifted from biomass formation to ethanol fermentation. The individual addition of two of the selected metabolites, fumaric acid and malic acid, reduced ethanol production at a higher dilution rate. This result demonstrates the potential for using metabolomics approaches to identify metabolites that reduce ethanol production in continuous culture at high dilution rates. Copyright © 2018 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Metabolome analysis for discovering biomarkers of gastroenterological cancer.

PubMed

Suzuki, Makoto; Nishiumi, Shin; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

2014-09-01

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Unambiguous metabolite identification in high-throughput metabolomics by hybrid 1D 1 H NMR/ESI MS 1 approach: Hybrid 1D 1 H NMR/ESI MS 1 metabolomics method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walker, Lawrence R.; Hoyt, David W.; Walker, S. Michael

We present a novel approach to improve accuracy of metabolite identification by combining direct infusion ESI MS1 with 1D 1H NMR spectroscopy. The new approach first applies standard 1D 1H NMR metabolite identification protocol by matching the chemical shift, J-coupling and intensity information of experimental NMR signals against the NMR signals of standard metabolites in metabolomics library. This generates a list of candidate metabolites. The list contains false positive and ambiguous identifications. Next, we constrained the list with the chemical formulas derived from high-resolution direct infusion ESI MS1 spectrum of the same sample. Detection of the signals of a metabolitemore » both in NMR and MS significantly improves the confidence of identification and eliminates false positive identification. 1D 1H NMR and direct infusion ESI MS1 spectra of a sample can be acquired in parallel in several minutes. This is highly beneficial for rapid and accurate screening of hundreds of samples in high-throughput metabolomics studies. In order to make this approach practical, we developed a software tool, which is integrated to Chenomx NMR Suite. The approach is demonstrated on a model mixture, tomato and Arabidopsis thaliana metabolite extracts, and human urine.« less

Cell-based metabolomics approach for assessing the impact of wastewater treatment plant effluent on downstream water quality

EPA Science Inventory

Wastewater treatment plants (WWTP) are a known source of various types of chemicals including pharmaceuticals and personal care products (PPCPs), naturally occurring hormones, and pesticides. There is great concern regarding their adverse effects on human and ecological health th...

Toxicological effects of cinnabar in rats by NMR-based metabolic profiling of urine and serum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei Lai; Liao Peiqiu; Wu Huifeng

2008-03-15

Cinnabar, an important traditional Chinese mineral medicine, has been widely used as a Chinese patent medicine ingredient for sedative therapy. However, the pharmaceutical and toxicological effects of cinnabar, especially in the whole organism, were subjected to few investigations. In this study, an NMR-based metabolomics approach has been applied to investigate the toxicological effects of cinnabar after intragastrical administration (dosed at 0.5, 2 and 5 g/kg body weight) on male Wistar rats. Liver and kidney histopathology examinations and serum clinical chemistry analyses were also performed. The {sup 1}H NMR spectra were analyzed using multivariate pattern recognition techniques to show the time-more » and dose-dependent biochemical variations induced by cinnabar. The metabolic signature of urinalysis from cinnabar-treated animals exhibited an increase in the levels of creatinine, acetate, acetoacetate, taurine, hippurate and phenylacetylglycine, together with a decrease in the levels of trimethyl-N-oxide, dimethylglycine and Kreb's cycle intermediates (citrate, 2-oxoglutarate and succinate). The metabolomics analyses of serum showed elevated concentrations of ketone bodies (3-D-hydroxybutyrate and acetoacetate), branched-chain amino acids (valine, leucine and isoleucine), choline and creatine as well as decreased glucose, lipids and lipoproteins from cinnabar-treated animals. These findings indicated cinnabar induced disturbance in energy metabolism, amino acid metabolism and gut microflora environment as well as slight injury in liver and kidney, which might indirectly result from cinnabar induced oxidative stress. This work illustrated the high reliability of NMR-based metabolomic approach on the study of the biochemical effects induced by traditional Chinese medicine.« less

Transcriptomic and metabolomic approaches to investigate the molecular responses of human cell lines exposed to the flame retardant hexabromocyclododecane (HBCD).

PubMed

Zhang, Jinkang; Williams, Timothy D; Abdallah, Mohamed Abou-Elwafa; Harrad, Stuart; Chipman, James K; Viant, Mark R

2015-12-01

The potential for human exposure to the brominated flame retardant, hexabromocyclododecane (HBCD) has given rise to health concerns, yet there is relatively limited knowledge about its possible toxic effects and the underlying molecular mechanisms that may mediate any impacts on health. In this study, unbiased transcriptomic and metabolomic approaches were employed to investigate the potential molecular changes that could lead to the toxicity of HBCD under concentrations relevant to human exposure conditions using in vitro models. A concentration-dependent cytotoxic effect of HBCD to A549 and HepG2/C3A cells was observed based on MTT assays or CCK-8 assays with EC50 values of 27.4 μM and 63.0 μM, respectively. Microarray-based transcriptomics and mass spectrometry-based metabolomics revealed few molecular changes in A549 cells or HepG2/C3A cells following a 24-hour exposure to several sub-lethal concentrations (2 to 4000 nM) of HBCD. Quantification of the level of HBCD in the HepG2/C3A exposed cells suggested that the flame retardant was present at concentrations several orders of magnitude higher than those reported to occur in human tissues. We conclude that at the concentrations known to be achievable following exposure in humans, HBCD exhibits no detectable acute toxicity in A549 cells, representative of the lung, or in HepG2/C3A cells, that are hepatocytes with some xenobiotic metabolic capacity. Copyright © 2015 Elsevier B.V. All rights reserved.

Assessing biological effects of fluoxetine in developing zebrafish embryos using gas chromatography-mass spectrometry based metabolomics.

PubMed

Mishra, Priti; Gong, Zhiyuan; Kelly, Barry C

2017-12-01

Continuous low-dose exposure of pharmaceutically active compounds (PhACs) in aquatic ecosystems is a concern worldwide. In this study, we utilized a gas chromatography mass spectrometry (GC-MS) based metabolomics approach to assess endogenous metabolite changes in developing zebrafish embryos exposed to different concentrations of the widely used antidepressant, fluoxetine. Embryos were exposed from 2 h post fertilization (hpf) until 96 hpf. Using the Fiehn GC-MS library, a total of 31 metabolites were positively identified in embryos. Statistical analyses revealed significant dysregulation of 11 metabolites in fluoxetine exposed embryos. Metabolite classes that were significantly altered included, amino acids, monosaccharides, glycerophosphates, fatty acids, carboxylic acid derivatives and sugars. Concentrations of amino acids, maltose, d-malic acid, 3-phosphoglycerate and d-glucose were significantly reduced in exposed embryos. Conversely, concentrations of citric acid were in some cases significantly elevated in exposed embryos. Metabolic pathway analysis revealed perturbation of five main pathways, including (i) alanine, aspartate and glutamate metabolism, (ii) phenylalanine, tyrosine and tryptophan biosynthesis, (iii) phenylalanine metabolism. (iv) tyrosine metabolism and (v) starch and sucrose metabolism. The results indicate fluoxetine exposure causes perturbation of energy and amino acid metabolism, which may adversely impact embryogenesis due to depletion of energy reserves during this period. Also, the observed alterations in aspartic acid, phenylalanine and tyrosine in fluoxetine exposed embryos suggests potential disruption of normal neurobehavioral and liver function. The results further demonstrate that GC-MS based metabolomics is an effective approach for assessing toxicodynamics and threshold effect levels of environmental pollutants in aquatic organisms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current Challenges in Plant Eco-Metabolomics

PubMed Central

Peters, Kristian; Worrich, Anja; Alka, Oliver; Balcke, Gerd; Bruelheide, Helge; Dietz, Sophie; Dührkop, Kai; Heinig, Uwe; Kücklich, Marlen; Müller, Caroline; Poeschl, Yvonne; Pohnert, Georg; Ruttkies, Christoph; Schweiger, Rabea; Shahaf, Nir; Tortosa, Maria; Ueberschaar, Nico; Velasco, Pablo; Weiß, Brigitte M.; van Dam, Nicole M.

2018-01-01

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology. PMID:29734799

Livestock metabolomics and the livestock metabolome: A systematic review

PubMed Central

Guo, An Chi; Sajed, Tanvir; Steele, Michael A.; Plastow, Graham S.; Wishart, David S.

2017-01-01

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed. PMID:28531195

Livestock metabolomics and the livestock metabolome: A systematic review.

PubMed

Goldansaz, Seyed Ali; Guo, An Chi; Sajed, Tanvir; Steele, Michael A; Plastow, Graham S; Wishart, David S

2017-01-01

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular "omics" approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

PubMed Central

Zhang, Shihua; Zhang, Liang; Tai, Yuling; Wang, Xuewen; Ho, Chi-Tang; Wan, Xiaochun

2018-01-01

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant. PMID:29915604

Field-Based Approach for Assessing the Impact of Treated Pulp and Paper Mill Effluent on Endogenous Metabolites of Fathead Minnows (Pimephales promelas)

EPA Science Inventory

A field-based metabolomic study was conducted during a shutdown of a pulp and paper mill (PPM) to assess the impacts of treated PPM effluent on endogenous polar metabolites in fathead minnow (FHM; Pimephales promelas) livers. Caged male and female FHMs were deployed at a Great La...

A Metabolomic Approach to the Study of Wine Micro-Oxygenation

PubMed Central

Arapitsas, Panagiotis; Scholz, Matthias; Vrhovsek, Urska; Di Blasi, Stefano; Biondi Bartolini, Alessandra; Masuero, Domenico; Perenzoni, Daniele; Rigo, Adelio; Mattivi, Fulvio

2012-01-01

Wine micro-oxygenation is a globally used treatment and its effects were studied here by analysing by untargeted LC-MS the wine metabolomic fingerprint. Eight different procedural variations, marked by the addition of oxygen (four levels) and iron (two levels) were applied to Sangiovese wine, before and after malolactic fermentation. Data analysis using supervised and unsupervised multivariate methods highlighted some known candidate biomarkers, together with a number of metabolites which had never previously been considered as possible biomarkers for wine micro-oxygenation. Various pigments and tannins were identified among the known candidate biomarkers. Additional new information was obtained suggesting a correlation between oxygen doses and metal contents and changes in the concentration of primary metabolites such as arginine, proline, tryptophan and raffinose, and secondary metabolites such as succinic acid and xanthine. Based on these findings, new hypotheses regarding the formation and reactivity of wine pigment during micro-oxygenation have been proposed. This experiment highlights the feasibility of using unbiased, untargeted metabolomic fingerprinting to improve our understanding of wine chemistry. PMID:22662221

The LC-MS-based metabolomics of hydroxytyrosol administration in rats reveals amelioration of the metabolic syndrome.

PubMed

Lemonakis, Nikolaos; Poudyal, Hemant; Halabalaki, Maria; Brown, Lindsay; Tsarbopoulos, Anthony; Skaltsounis, Alexios-Leandros; Gikas, Evagelos

2017-01-15

Hydroxytyrosol (HT), an important component of olive fruit and olive oil, improves the signs of metabolic syndrome in rats following chronic treatment. At a dose of 20mg/kg/day, HT decreased adiposity and improved cardiovascular and liver structure and function in rats fed with a high-carbohydrate, high-fat diet. An untargeted metabolomics approach has been employed using both UPLC-Orbitrap and -QqTOF methods to identify the changes induced by chronic HT administration on the plasma metabolome. 31 metabolites have been found to be differentially expressed between the examined groups. HT was shown to decrease biosynthesis of unsaturated fatty acids, fatty acid biosynthesis, and the metabolism of linoleic acid, retinol, sphingolipids and arachidonic acid, whereas glycerolipid metabolism is up-regulated. These are plausible mechanisms for the attenuation by HT of cardiovascular, liver and metabolic changes in high-carbohydrate, high-fat diet fed rats. Copyright © 2016 Elsevier B.V. All rights reserved.

Dendrobium huoshanense polysaccharide prevents ethanol-induced liver injury in mice by metabolomic analysis.

PubMed

Wang, Xiao-Yu; Luo, Jian-Ping; Chen, Rui; Zha, Xue-Qiang; Pan, Li-Hua

2015-01-01

The prevalence of alcohol consumption has increased in modern dietary life and alcoholic liver injury can follow. Dendrobium huoshanense polysaccharide (DHP) is a homogeneous polysaccharide isolated from Dendrobium huoshanense, which possesses hepatoprotection function. In this study, we investigated the metabolic profiles of serum and liver tissues extracts from control, ethanol-treated and DHP\\ethanol-treated mice using a UHPLC/LTQ Orbitrap XL MS-based metabolomics approach. Our results indicated that DHP alleviated early steatosis and inflammation in liver histology and the metabolomic analysis of serum and hepatic tissue revealed that first, ethanol treatment mainly altered phosphatidylcholines (PCs) including PC (13:0) and phosphocholine, arachidonic acid metabolites including 20-ethyl PGF2α and amino acids including L-Proline; Second, DHP supplementation ameliorated the altered metabolic levels particularly involved in phosphocholine and L-Proline. These data suggested that DHP might restore the perturbed metabolism pathways by ethanol exposure to prevent the progression of alcoholic liver injury. Copyright © 2015 Elsevier B.V. All rights reserved.

Metabolomics in Toxicology and Preclinical Research

PubMed Central

Ramirez, Tzutzuy; Daneshian, Mardas; Kamp, Hennicke; Bois, Frederic Y.; Clench, Malcolm R.; Coen, Muireann; Donley, Beth; Fischer, Steven M.; Ekman, Drew R.; Fabian, Eric; Guillou, Claude; Heuer, Joachim; Hogberg, Helena T.; Jungnickel, Harald; Keun, Hector C.; Krennrich, Gerhard; Krupp, Eckart; Luch, Andreas; Noor, Fozia; Peter, Erik; Riefke, Bjoern; Seymour, Mark; Skinner, Nigel; Smirnova, Lena; Verheij, Elwin; Wagner, Silvia; Hartung, Thomas; van Ravenzwaay, Bennard; Leist, Marcel

2013-01-01

Summary Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context. PMID:23665807

The application of metabolomics in traditional Chinese medicine opens up a dialogue between Chinese and Western medicine.

PubMed

Cao, Hongxin; Zhang, Aihua; Zhang, Huamin; Sun, Hui; Wang, Xijun

2015-02-01

Metabolomics provides an opportunity to develop the systematic analysis of the metabolites and has been applied to discovering biomarkers and perturbed pathways which can clarify the action mechanism of traditional Chinese medicines (TCM). TCM is a comprehensive system of medical practice that has been used to diagnose, treat and prevent illnesses more than 3000 years. Metabolomics represents a powerful approach that provides a dynamic picture of the phenotype of biosystems through the study of endogenous metabolites, and its methods resemble those of TCM. Recently, metabolomics tools have been used for facilitating interactional effects of both Western medicine and TCM. We describe a protocol for investigating how metabolomics can be used to open up 'dialogue' between Chinese and Western medicine, and facilitate lead compound discovery and development from TCM. Metabolomics will bridge the cultural gap between TCM and Western medicine and improve development of integrative medicine, and maximally benefiting the human. Copyright © 2014 John Wiley & Sons, Ltd.

Cell-based Therapy for Acute Organ Injury: Preclinical Evidence and On-going Clinical Trials Using Mesenchymal Stem Cells

PubMed Central

Monsel, Antoine; Zhu, Ying-gang; Gennai, Stephane; Hao, Qi; Liu, Jia; Lee, Jae W.

2014-01-01

Critically ill patients often suffer from multiple organ failures involving lung, kidney, liver or brain. Genomic, proteomic and metabolomic approaches highlight common injury mechanisms leading to acute organ failure. This underlines the need to focus on therapeutic strategies affecting multiple injury pathways. The use of adult stem cells such as mesenchymal stem or stromal cells (MSC) may represent a promising new therapeutic approach as increasing evidence shows that MSC can exert protective effects following injury through the release of pro-mitotic, anti-apoptotic, anti-inflammatory and immunomodulatory soluble factors. Furthermore, they can mitigate metabolomic and oxidative stress imbalance. In this work, we review the biological capabilities of MSC and the results of clinical trials using MSC as therapy in acute organ injuries. Although preliminary results are encouraging, more studies concerning safety and efficacy of MSC therapy are needed to determine their optimal clinical use. PMID:25211170

Metabolomic approach for determination of key volatile compounds related to beef flavor in glutathione-Maillard reaction products.

PubMed

Lee, Sang Mi; Kwon, Goo Young; Kim, Kwang-Ok; Kim, Young-Suk

2011-10-10

The non-targeted analysis, combining gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF/MS) and sensory evaluation, was applied to investigate the relationship between volatile compounds and the sensory attributes of glutathione-Maillard reaction products (GSH-MRPs) prepared under different reaction conditions. Volatile compounds in GSH-MRPs correlating to the sensory attributes were determined using partial least-squares (PLS) regression. Volatile compounds such as 2-methylfuran-3-thiol, 3-sulfanylpentan-2-one, furan-2-ylmethanethiol, 2-propylpyrazine, 1-furan-2-ylpropan-2-one, 1H-pyrrole, 2-methylthiophene, and 2-(furan-2-ylmethyldisulfanylmethyl)furan could be identified as possible key contributors to the beef-related attributes of GSH-MRPs. In this study, we demonstrated that the unbiased non-targeted analysis based on metabolomic approach allows the identification of key volatile compounds related to beef flavor in GSH-MRPs. Copyright © 2011 Elsevier B.V. All rights reserved.

[Application of Mass Spectrometry to the Diagnosis of Cancer--Chairman's Introductory Remarks].

PubMed

Yatomi, Yutaka

2015-09-01

In this symposium, the latest application of mass spectrometry to laboratory medicine, i.e., to the early diagnosis of cancer, was introduced. Dr. Masaru YOSHIDA, who has been using metabolome analysis to discover biomarker candidates for gastroenterological diseases, presented an automated early diagnosis system for early stages of colon cancer based on metabolome analysis and using a minute amount of blood. On the other hand, Dr. Sen TAKEDA, who has developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis, presented a device for the clinical diagnosis of cancer using probe electrospray ionization (PESI) and machine-learning called the dual penalized logistic regression machine (dPLRM).

Recent advances in liquid and gas chromatography methodology for extending coverage of the metabolome.

PubMed

Haggarty, Jennifer; Burgess, Karl Ev

2017-02-01

The metabolome is the complete complement of metabolites (small organic biomolecules). In order to comprehensively understand the effect of stimuli on a biological system, it is important to detect as many of the metabolites within that system as possible. This review briefly describes some new advances in liquid and gas chromatography to improve coverage of the metabolome, including the serial combination of two columns in tandem, column switching and different variations of two-dimensional chromatography. Supercritical fluid chromatography could provide complimentary data to liquid and gas chromatography. Although there have been many recent advancements in the field of metabolomics, it is evident that a combination, rather than a single method, is required to approach full coverage of the metabolome. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics.

PubMed

Hamzeiy, Hamid; Cox, Jürgen

2017-02-01

Computational workflows for mass spectrometry-based shotgun proteomics and untargeted metabolomics share many steps. Despite the similarities, untargeted metabolomics is lagging behind in terms of reliable fully automated quantitative data analysis. We argue that metabolomics will strongly benefit from the adaptation of successful automated proteomics workflows to metabolomics. MaxQuant is a popular platform for proteomics data analysis and is widely considered to be superior in achieving high precursor mass accuracies through advanced nonlinear recalibration, usually leading to five to ten-fold better accuracy in complex LC-MS/MS runs. This translates to a sharp decrease in the number of peptide candidates per measured feature, thereby strongly improving the coverage of identified peptides. We argue that similar strategies can be applied to untargeted metabolomics, leading to equivalent improvements in metabolite identification. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Metabolome Integrated Analysis of High-Temperature Response in Pinus radiata.

PubMed

Escandón, Mónica; Meijón, Mónica; Valledor, Luis; Pascual, Jesús; Pinto, Gloria; Cañal, María Jesús

2018-01-01

The integrative omics approach is crucial to identify the molecular mechanisms underlying high-temperature response in non-model species. Based on future scenarios of heat increase, Pinus radiata plants were exposed to a temperature of 40°C for a period of 5 days, including recovered plants (30 days after last exposure to 40°C) in the analysis. The analysis of the metabolome using complementary mass spectrometry techniques (GC-MS and LC-Orbitrap-MS) allowed the reliable quantification of 2,287 metabolites. The analysis of identified metabolites and highlighter metabolic pathways across heat time exposure reveal the dynamism of the metabolome in relation to high-temperature response in P. radiata , identifying the existence of a turning point (on day 3) at which P. radiata plants changed from an initial stress response program (shorter-term response) to an acclimation one (longer-term response). Furthermore, the integration of metabolome and physiological measurements, which cover from the photosynthetic state to hormonal profile, suggests a complex metabolic pathway interaction network related to heat-stress response. Cytokinins (CKs), fatty acid metabolism and flavonoid and terpenoid biosynthesis were revealed as the most important pathways involved in heat-stress response in P. radiata , with zeatin riboside (ZR) and isopentenyl adenosine (iPA) as the key hormones coordinating these multiple and complex interactions. On the other hand, the integrative approach allowed elucidation of crucial metabolic mechanisms involved in heat response in P. radiata , as well as the identification of thermotolerance metabolic biomarkers (L-phenylalanine, hexadecanoic acid, and dihydromyricetin), crucial metabolites which can reschedule the metabolic strategy to adapt to high temperature.

Metabolome Integrated Analysis of High-Temperature Response in Pinus radiata

PubMed Central

Escandón, Mónica; Meijón, Mónica; Valledor, Luis; Pascual, Jesús; Pinto, Gloria; Cañal, María Jesús

2018-01-01

The integrative omics approach is crucial to identify the molecular mechanisms underlying high-temperature response in non-model species. Based on future scenarios of heat increase, Pinus radiata plants were exposed to a temperature of 40°C for a period of 5 days, including recovered plants (30 days after last exposure to 40°C) in the analysis. The analysis of the metabolome using complementary mass spectrometry techniques (GC-MS and LC-Orbitrap-MS) allowed the reliable quantification of 2,287 metabolites. The analysis of identified metabolites and highlighter metabolic pathways across heat time exposure reveal the dynamism of the metabolome in relation to high-temperature response in P. radiata, identifying the existence of a turning point (on day 3) at which P. radiata plants changed from an initial stress response program (shorter-term response) to an acclimation one (longer-term response). Furthermore, the integration of metabolome and physiological measurements, which cover from the photosynthetic state to hormonal profile, suggests a complex metabolic pathway interaction network related to heat-stress response. Cytokinins (CKs), fatty acid metabolism and flavonoid and terpenoid biosynthesis were revealed as the most important pathways involved in heat-stress response in P. radiata, with zeatin riboside (ZR) and isopentenyl adenosine (iPA) as the key hormones coordinating these multiple and complex interactions. On the other hand, the integrative approach allowed elucidation of crucial metabolic mechanisms involved in heat response in P. radiata, as well as the identification of thermotolerance metabolic biomarkers (L-phenylalanine, hexadecanoic acid, and dihydromyricetin), crucial metabolites which can reschedule the metabolic strategy to adapt to high temperature. PMID:29719546

Metabolomic Fingerprinting of Romaneschi Globe Artichokes by NMR Spectroscopy and Multivariate Data Analysis.

PubMed

de Falco, Bruna; Incerti, Guido; Pepe, Rosa; Amato, Mariana; Lanzotti, Virginia

2016-09-01

Globe artichoke (Cynara cardunculus L. var. scolymus L. Fiori) and cardoon (Cynara cardunculus L. var. altilis DC) are sources of nutraceuticals and bioactive compounds. To apply a NMR metabolomic fingerprinting approach to Cynara cardunculus heads to obtain simultaneous identification and quantitation of the major classes of organic compounds. The edible part of 14 Globe artichoke populations, belonging to the Romaneschi varietal group, were extracted to obtain apolar and polar organic extracts. The analysis was also extended to one species of cultivated cardoon for comparison. The (1) H-NMR of the extracts allowed simultaneous identification of the bioactive metabolites whose quantitation have been obtained by spectral integration followed by principal component analysis (PCA). Apolar organic extracts were mainly based on highly unsaturated long chain lipids. Polar organic extracts contained organic acids, amino acids, sugars (mainly inulin), caffeoyl derivatives (mainly cynarin), flavonoids, and terpenes. The level of nutraceuticals was found to be highest in the Italian landraces Bianco di Pertosa zia E and Natalina while cardoon showed the lowest content of all metabolites thus confirming the genetic distance between artichokes and cardoon. Metabolomic approach coupling NMR spectroscopy with multivariate data analysis allowed for a detailed metabolite profile of artichoke and cardoon varieties to be obtained. Relevant differences in the relative content of the metabolites were observed for the species analysed. This work is the first application of (1) H-NMR with multivariate statistics to provide a metabolomic fingerprinting of Cynara scolymus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future.

PubMed

Rankin, Naomi J; Preiss, David; Welsh, Paul; Sattar, Naveed

2016-10-01

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

A METABOLOMIC APPROACH TO UNDERSTANDING ENDOCRINE DISRUPTION IN FATHEAD MINNOW

EPA Science Inventory

Although widely used in the study of rodent toxicity responses to assess human risk, metabolomics is now finding utility in toxicity assessments in a wide variety of other organisms including environmentally relevant small fish species such as fathead minnow (FHM) and medaka. To...

Metabolomics: A Window for Understanding Long Term Physical Consequences of Distrubed Sleep and Hypothalamic Pituitary Adrenal Function in Posttraumatic Stress

DTIC Science & Technology

2017-09-01

performed on pre -collected plasma samples from a study that had a two- group cross-sectional design in which main comparisons were with medically...controls. Approach Metabolomic analysis will be performed on pre -collected plasma samples from a study that had a two- group cross-sectional design in...disturbances, and health. Metabolomic analysis will be performed on pre -collected plasma samples from a study that had a two- group cross-sectional

Vitamins, Metabolomics and Prostate Cancer

PubMed Central

Mondul, Alison M; Weinstein, Stephanie J; Albanes, Demetrius

2016-01-01

Purpose How micronutrients might influence risk of developing adenocarcinoma of the prostate has been the focus of a large body of research (especially regarding vitamins E, A, and D). Metabolomic profiling has the potential to discover molecular species relevant to prostate cancer etiology, early detection, and prevention, and may help elucidate the biologic mechanisms by which vitamins influence prostate cancer risk. Methods Prostate cancer risk data related to vitamins E, A, and D and metabolomics profiling from clinical, cohort, and nested case-control studies, along with randomized controlled trials, are examined and summarized, along with recent metabolomic data of the vitamin phenotypes. Results Higher vitamin E serologic status is associated with lower prostate cancer risk, and vitamin E genetic variant data support this. By contrast, controlled vitamin E supplementation trials have mixed results based on differing designs and dosages. Beta-carotene supplementation (in smokers) and higher circulating retinol and 25-hydroxy-vitamin D concentrations appear related to elevated prostate cancer risk. Our prospective metabolomics profiling of fasting serum collected 1-20 years prior to clinical diagnoses found lipid and energy/TCA cycle metabolites, including inositol-1-phosphate, lysolipids, alpha-ketoglutarate, and citrate, significantly associated with risk of aggressive disease. Conclusions Several active leads exist regarding the role of micronutrients and metabolites in prostate cancer carcinogenesis and risk. How vitamins D and A may adversely impact risk, and whether low-dose vitamin E supplementation remains a viable preventive approach, require further study. PMID:27339624

Vitamins, metabolomics, and prostate cancer.

PubMed

Mondul, Alison M; Weinstein, Stephanie J; Albanes, Demetrius

2017-06-01

How micronutrients might influence risk of developing adenocarcinoma of the prostate has been the focus of a large body of research (especially regarding vitamins E, A, and D). Metabolomic profiling has the potential to discover molecular species relevant to prostate cancer etiology, early detection, and prevention, and may help elucidate the biologic mechanisms through which vitamins influence prostate cancer risk. Prostate cancer risk data related to vitamins E, A, and D and metabolomic profiling from clinical, cohort, and nested case-control studies, along with randomized controlled trials, are examined and summarized, along with recent metabolomic data of the vitamin phenotypes. Higher vitamin E serologic status is associated with lower prostate cancer risk, and vitamin E genetic variant data support this. By contrast, controlled vitamin E supplementation trials have had mixed results based on differing designs and dosages. Beta-carotene supplementation (in smokers) and higher circulating retinol and 25-hydroxy-vitamin D concentrations appear related to elevated prostate cancer risk. Our prospective metabolomic profiling of fasting serum collected 1-20 years prior to clinical diagnoses found reduced lipid and energy/TCA cycle metabolites, including inositol-1-phosphate, lysolipids, alpha-ketoglutarate, and citrate, significantly associated with lower risk of aggressive disease. Several active leads exist regarding the role of micronutrients and metabolites in prostate cancer carcinogenesis and risk. How vitamins D and A may adversely impact risk, and whether low-dose vitamin E supplementation remains a viable preventive approach, require further study.

LC-MS metabolic profiling of Arabidopsis thaliana plant leaves and cell cultures: optimization of pre-LC-MS procedure parameters.

PubMed

t'Kindt, Ruben; De Veylder, Lieven; Storme, Michael; Deforce, Dieter; Van Bocxlaer, Jan

2008-08-01

This study treats the optimization of methods for homogenizing Arabidopsis thaliana plant leaves as well as cell cultures, and extracting their metabolites for metabolomics analysis by conventional liquid chromatography electrospray ionization mass spectrometry (LC-ESI/MS). Absolute recovery, process efficiency and procedure repeatability have been compared between different pre-LC-MS homogenization/extraction procedures through the use of samples fortified before extraction with a range of representative metabolites. Hereby, the magnitude of the matrix effect observed in the ensuing LC-MS based metabolomics analysis was evaluated. Based on relative recovery and repeatability of key metabolites, comprehensiveness of extraction (number of m/z-retention time pairs) and clean-up potential of the approach (minimum matrix effects), the most appropriate sample pre-treatment was adopted. It combines liquid nitrogen homogenization for plant leaves with thermomixer based extraction using MeOH/H(2)O 80/20. As such, an efficient and highly reproducible LC-MS plant metabolomics set-up is achieved, as illustrated by the obtained results for both LC-MS (8.88%+/-5.16 versus 7.05%+/-4.45) and technical variability (12.53%+/-11.21 versus 9.31%+/-6.65) data in a comparative investigation of A. thaliana plant leaves and cell cultures, respectively.

Metabolomics-Based Discovery of Small Molecule Biomarkers in Serum Associated with Dengue Virus Infections and Disease Outcomes

PubMed Central

Voge, Natalia V.; Perera, Rushika; Mahapatra, Sebabrata; Gresh, Lionel; Balmaseda, Angel; Loroño-Pino, María A.; Hopf-Jannasch, Amber S.; Belisle, John T.; Harris, Eva; Blair, Carol D.; Beaty, Barry J.

2016-01-01

Background Epidemic dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) are overwhelming public health capacity for diagnosis and clinical care of dengue patients throughout the tropical and subtropical world. The ability to predict severe dengue disease outcomes (DHF/DSS) using acute phase clinical specimens would be of enormous value to physicians and health care workers for appropriate triaging of patients for clinical management. Advances in the field of metabolomics and analytic software provide new opportunities to identify host small molecule biomarkers (SMBs) in acute phase clinical specimens that differentiate dengue disease outcomes. Methodology/Principal Findings Exploratory metabolomic studies were conducted to characterize the serum metabolome of patients who experienced different dengue disease outcomes. Serum samples from dengue patients from Nicaragua and Mexico were retrospectively obtained, and hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry (MS) identified small molecule metabolites that were associated with and statistically differentiated DHF/DSS, DF, and non-dengue (ND) diagnosis groups. In the Nicaraguan samples, 191 metabolites differentiated DF from ND outcomes and 83 differentiated DHF/DSS and DF outcomes. In the Mexican samples, 306 metabolites differentiated DF from ND and 37 differentiated DHF/DSS and DF outcomes. The structural identities of 13 metabolites were confirmed using tandem mass spectrometry (MS/MS). Metabolomic analysis of serum samples from patients diagnosed as DF who progressed to DHF/DSS identified 65 metabolites that predicted dengue disease outcomes. Differential perturbation of the serum metabolome was demonstrated following infection with different DENV serotypes and following primary and secondary DENV infections. Conclusions/Significance These results provide proof-of-concept that a metabolomics approach can be used to identify metabolites or SMBs in serum specimens that are associated with distinct DENV infections and disease outcomes. The differentiating metabolites also provide insights into metabolic pathways and pathogenic and immunologic mechanisms associated with dengue disease severity. PMID:26913918

Integrated work-flow for quantitative metabolome profiling of plants, Peucedani Radix as a case.

PubMed

Song, Yuelin; Song, Qingqing; Liu, Yao; Li, Jun; Wan, Jian-Bo; Wang, Yitao; Jiang, Yong; Tu, Pengfei

2017-02-08

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. Copyright © 2016 Elsevier B.V. All rights reserved.

Multiplatform Metabolomics Investigation of Antiadipogenic Effects on 3T3-L1 Adipocytes by a Potent Diarylheptanoid.

PubMed

Du, Dan; Gu, Haiwei; Djukovic, Danijel; Bettcher, Lisa; Gong, Meng; Zheng, Wen; Hu, Liqiang; Zhang, Xinyu; Zhang, Renke; Wang, Dongfang; Raftery, Daniel

2018-06-01

Obesity is fast becoming a serious health problem worldwide. Of the many possible antiobesity strategies, one interesting approach focuses on blocking adipocyte differentiation and lipid accumulation to counteract the rise in fat storage. However, there is currently no drug available for the treatment of obesity that works by inhibiting adipocyte differentiation. Here we use a broad-based metabolomics approach to interrogate and better understand metabolic changes that occur during adipocyte differentiation. In particular, we focus on changes induced by the antiadipogenic diarylheptanoid, which was isolated from a traditional Chinese medicine Dioscorea zingiberensis and identified as (3 R,5 R)-3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane (1). Targeted aqueous metabolic profiling indicated that a total of 14 metabolites involved in the TCA cycle, glycolysis, amino acid metabolism, and purine catabolism participate in regulating energy metabolism, lipogenesis, and lipolysis in adipocyte differentiation and can be modulated by diarylheptanoid 1. As indicated by lipidomics analysis, diarylheptanoid 1 restored the quantity and degree of unsaturation of long-chain free fatty acids and restored the levels of 171 lipids mainly from 10 lipid classes in adipocytes. In addition, carbohydrate metabolism in diarylheptanoid-1-treated adipocytes further demonstrated the delayed differentiation process by flux analysis. Our results provide valuable information for further understanding the metabolic adjustment in adipocytes subjected to diarylheptanoid 1 treatment. Moreover, this study offers new insight into developing antiadipogenic leading compounds based on metabolomics.

Metabolic discrimination of sea buckthorn from different Hippophaë species by 1H NMR based metabolomics.

PubMed

Liu, Yue; Fan, Gang; Zhang, Jing; Zhang, Yi; Li, Jingjian; Xiong, Chao; Zhang, Qi; Li, Xiaodong; Lai, Xianrong

2017-05-08

Sea buckthorn (Hippophaë; Elaeagnaceae) berries are widely consumed in traditional folk medicines, nutraceuticals, and as a source of food. The growing demand of sea buckthorn berries and morphological similarity of Hippophaë species leads to confusions, which might cause misidentification of plants used in natural products. Detailed information and comparison of the complete set of metabolites of different Hippophaë species are critical for their objective identification and quality control. Herein, the variation among seven species and seven subspecies of Hippophaë was studied using proton nuclear magnetic resonance ( 1 H NMR) metabolomics combined with multivariate data analysis, and the important metabolites were quantified by quantitative 1 H NMR (qNMR) method. The results showed that different Hippophaë species can be clearly discriminated and the important interspecific discriminators, including organic acids, L-quebrachitol, and carbohydrates were identified. Statistical differences were found among most of the Hippophaë species and subspecies at the content levels of the aforementioned interspecific discriminators via qNMR and one-way analysis of variance (ANOVA) test. These findings demonstrated that 1 H NMR-based metabolomics is an applicable and effective approach for simultaneous metabolic profiling, species differentiation and quality assessment.

Haystack, a web-based tool for metabolomics research

PubMed Central

2014-01-01

Background Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. Results To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Conclusion Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods. PMID:25350247

Haystack, a web-based tool for metabolomics research.

PubMed

Grace, Stephen C; Embry, Stephen; Luo, Heng

2014-01-01

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

NMR-based Metabolomics Applications in Biological and Environmental Science

EPA Science Inventory

As a complimentary tool to other omics platforms, metabolomics is increasingly being used bybiologists to study the dynamic response of biological systems (cells, tissues, or wholeorganisms) under diverse physiological or pathological conditions. Metabolomics deals with the quali...

Clinical metabolomics paves the way towards future healthcare strategies

PubMed Central

Collino, Sebastiano; Martin, François‐Pierre J.; Rezzi, Serge

2013-01-01

Metabolomics is recognized as a powerful top‐down system biological approach to understand genetic‐environment‐health paradigms paving new avenues to identify clinically relevant biomarkers. It is nowadays commonly used in clinical applications shedding new light on physiological regulatory processes of complex mammalian systems with regard to disease aetiology, diagnostic stratification and, potentially, mechanism of action of therapeutic solutions. A key feature of metabolomics lies in its ability to underpin the complex metabolic interactions of the host with its commensal microbial partners providing a new way to define individual and population phenotypes. This review aims at describing recent applications of metabolomics in clinical fields with insight into diseases, diagnostics/monitoring and improvement of homeostatic metabolic regulation. PMID:22348240

Metabolomics in diabetes, a review.

PubMed

Pallares-Méndez, Rigoberto; Aguilar-Salinas, Carlos A; Cruz-Bautista, Ivette; Del Bosque-Plata, Laura

2016-01-01

Metabolomics is a promising approach for the identification of chemical compounds that serve for early detection, diagnosis, prediction of therapeutic response and prognosis of disease. Moreover, metabolomics has shown to increase the diagnostic threshold and prediction of type 2 diabetes. Evidence suggests that branched-chain amino acids, acylcarnitines and aromatic amino acids may play an early role on insulin resistance, exposing defects on amino acid metabolism, β-oxidation, and tricarboxylic acid cycle. This review aims to provide a panoramic view of the metabolic shifts that antecede or follow type 2 diabetes. Key messages BCAAs, AAAs and acylcarnitines are strongly associated with early insulin resistance. Diabetes risk prediction has been improved when adding metabolomic markers of dysglycemia to standard clinical and biochemical factors.

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data

PubMed Central

Barnes, Stephen; Benton, H. Paul; Casazza, Krista; Cooper, Sara J.; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H.; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K.; Renfrow, Matthew B.; Tiwari, Hemant K.

2016-01-01

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program’s goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. PMID:27434804

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report.

PubMed

Bowler, Russell P; Wendt, Chris H; Fessler, Michael B; Foster, Matthew W; Kelly, Rachel S; Lasky-Su, Jessica; Rogers, Angela J; Stringer, Kathleen A; Winston, Brent W

2017-12-01

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.

PubMed

Grapov, Dmitry; Wanichthanarak, Kwanjeera; Fiehn, Oliver

2015-08-15

Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. ofiehn@ucdavis.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effects of Perfluorooctanoic Acid on Metabolic Profiles in Brain and Liver of Mouse Revealed by a High-throughput Targeted Metabolomics Approach

NASA Astrophysics Data System (ADS)

Yu, Nanyang; Wei, Si; Li, Meiying; Yang, Jingping; Li, Kan; Jin, Ling; Xie, Yuwei; Giesy, John P.; Zhang, Xiaowei; Yu, Hongxia

2016-04-01

Perfluorooctanoic acid (PFOA), a perfluoroalkyl acid, can result in hepatotoxicity and neurobehavioral effects in animals. The metabolome, which serves as a connection among transcriptome, proteome and toxic effects, provides pathway-based insights into effects of PFOA. Since understanding of changes in the metabolic profile during hepatotoxicity and neurotoxicity were still incomplete, a high-throughput targeted metabolomics approach (278 metabolites) was used to investigate effects of exposure to PFOA for 28 d on brain and liver of male Balb/c mice. Results of multivariate statistical analysis indicated that PFOA caused alterations in metabolic pathways in exposed individuals. Pathway analysis suggested that PFOA affected metabolism of amino acids, lipids, carbohydrates and energetics. Ten and 18 metabolites were identified as potential unique biomarkers of exposure to PFOA in brain and liver, respectively. In brain, PFOA affected concentrations of neurotransmitters, including serotonin, dopamine, norepinephrine, and glutamate in brain, which provides novel insights into mechanisms of PFOA-induced neurobehavioral effects. In liver, profiles of lipids revealed involvement of β-oxidation and biosynthesis of saturated and unsaturated fatty acids in PFOA-induced hepatotoxicity, while alterations in metabolism of arachidonic acid suggesting potential of PFOA to cause inflammation response in liver. These results provide insight into the mechanism and biomarkers for PFOA-induced effects.

Mortality prediction in patients with severe septic shock: a pilot study using a target metabolomics approach.

PubMed

Ferrario, Manuela; Cambiaghi, Alice; Brunelli, Laura; Giordano, Silvia; Caironi, Pietro; Guatteri, Luca; Raimondi, Ferdinando; Gattinoni, Luciano; Latini, Roberto; Masson, Serge; Ristagno, Giuseppe; Pastorelli, Roberta

2016-02-05

Septic shock remains a major problem in Intensive Care Unit, with high lethality and high-risk second lines treatments. In this preliminary retrospective investigation we examined plasma metabolome and clinical features in a subset of 20 patients with severe septic shock (SOFA score >8), enrolled in the multicenter Albumin Italian Outcome Sepsis study (ALBIOS, NCT00707122). Our purpose was to evaluate the changes of circulating metabolites in relation to mortality as a pilot study to be extended in a larger cohort. Patients were analyzed according to their 28-days and 90-days mortality. Metabolites were measured using a targeted mass spectrometry-based quantitative metabolomic approach that included acylcarnitines, aminoacids, biogenic amines, glycerophospholipids, sphingolipids, and sugars. Data-mining techniques were applied to evaluate the association of metabolites with mortality. Low unsaturated long-chain phosphatidylcholines and lysophosphatidylcholines species were associated with long-term survival (90-days) together with circulating kynurenine. Moreover, a decrease of these glycerophospholipids was associated to the event at 28-days and 90-days in combination with clinical variables such as cardiovascular SOFA score (28-day mortality model) or renal replacement therapy (90-day mortality model). Early changes in the plasma levels of both lipid species and kynurenine associated with mortality have potential implications for early intervention and discovering new target therapy.

Mortality prediction in patients with severe septic shock: a pilot study using a target metabolomics approach

PubMed Central

Ferrario, Manuela; Cambiaghi, Alice; Brunelli, Laura; Giordano, Silvia; Caironi, Pietro; Guatteri, Luca; Raimondi, Ferdinando; Gattinoni, Luciano; Latini, Roberto; Masson, Serge; Ristagno, Giuseppe; Pastorelli, Roberta

2016-01-01

Septic shock remains a major problem in Intensive Care Unit, with high lethality and high-risk second lines treatments. In this preliminary retrospective investigation we examined plasma metabolome and clinical features in a subset of 20 patients with severe septic shock (SOFA score >8), enrolled in the multicenter Albumin Italian Outcome Sepsis study (ALBIOS, NCT00707122). Our purpose was to evaluate the changes of circulating metabolites in relation to mortality as a pilot study to be extended in a larger cohort. Patients were analyzed according to their 28-days and 90-days mortality. Metabolites were measured using a targeted mass spectrometry-based quantitative metabolomic approach that included acylcarnitines, aminoacids, biogenic amines, glycerophospholipids, sphingolipids, and sugars. Data-mining techniques were applied to evaluate the association of metabolites with mortality. Low unsaturated long-chain phosphatidylcholines and lysophosphatidylcholines species were associated with long-term survival (90-days) together with circulating kynurenine. Moreover, a decrease of these glycerophospholipids was associated to the event at 28-days and 90-days in combination with clinical variables such as cardiovascular SOFA score (28-day mortality model) or renal replacement therapy (90-day mortality model). Early changes in the plasma levels of both lipid species and kynurenine associated with mortality have potential implications for early intervention and discovering new target therapy. PMID:26847922

An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer.

PubMed

Ma, Yanlei; Zhang, Peng; Wang, Feng; Liu, Weijie; Yang, Jianjun; Qin, Huanlong

2012-04-01

The present study was designed to search for potential diagnostic biomarkers in the serum of colorectal cancer (CRC). CRC is the third most common cancer worldwide, and its prognosis is poor at early stages. A panel of novel biomarkers is urgently needed for early diagnosis of CRC. An integrated proteomics and metabolomics approach was performed to define oncofetal biomarkers in CRC by protein and metabolite profiling of serum samples from CRC patients, healthy control adults, and fetus. The differentially expressed proteins were identified by a 2-D DIGE (2-Dimensional Difference Gel Electrophoresis) coupled with a Finnigan LTQ-based proteomics approach. Meanwhile, the serum metabolome was analyzed using gas chromatography-mass spectrometry integrated with a commercial mass spectral library for peak identification. Of the 28 identified proteins and the 34 analyzed metabolites, only 5 protein spots and 6 metabolites were significantly increased or decreased in both CRC and fetal serum groups compared with the healthy adult group. Data from supervised predictive models allowed a separation of 93.5% of CRC patients from the healthy controls using the 6 metabolites. Finally, correlation analysis was applied to establish quantitative linkages between the 5 individual metabolite 3-hydroxybutyric acid, L-valine, L-threonine, 1-deoxyglucose, and glycine and the 5 individual proteins MACF1, APOH, A2M, IGL@, and VDB. Furthermore, 10 potential oncofetal biomarkers were characterized and their potential for CRC diagnosis was validated. The integrated approach we developed will promote the translation of biomarkers with clinical value into routine clinical practice.

Applications of NMR-based metabolomics in biological and environmental research

EPA Science Inventory

As a complimentary tool to other omics platforms, metabolomics is increasingly being used by biologists to study the dynamic response of biological systems (cells, tissues, or whole organisms) under diverse physiological or pathological conditions. Metabolomics deals with the qu...

Metabolomic Profiles of Current Cigarette Smokers

PubMed Central

Hsu, Ping-Ching; Lan, Renny S.; Brasky, Theodore M.; Marian, Catalin; Cheema, Amrita K.; Ressom, Habtom W.; Loffredo, Christopher A.; Pickworth, Wallace B.; Shields, Peter G.

2017-01-01

Smoking-related biomarkers for lung cancer and other diseases are needed to enhance early detection strategies and to provide a science base for tobacco product regulation. An untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF MS) totaling 957 assays was used in a novel experimental design where 105 current smokers smoked 2 cigarettes one hour apart. Blood was collected immediately before and after each cigarette allowing for within-subject replication. Dynamic changes of the metabolomic profiles from smokers’ four blood samples were observed and biomarkers affected by cigarette smoking were identified. Thirty-one metabolites were definitively shown to be affected by acute effect of cigarette smoking, uniquely including menthol-glucuronide, the reduction of glutamate, oleamide, and 13 glycerophospholipids. This first time identification of a menthol metabolite in smokers’ blood serves as proof-of-principle for using metabolomics to identify new tobacco-exposure biomarkers, and also provides new opportunities in studying menthol-containing tobacco products in humans. Gender and race differences also were observed. Network analysis revealed 12 molecules involved in cancer, notably inhibition of cAMP. These novel tobacco-related biomarkers provide new insights to the effects of smoking which may be important in carcinogenesis but not previously linked with tobacco-related diseases. PMID:27341184

Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study.

PubMed

Madrid-Gambin, Francisco; Llorach, Rafael; Vázquez-Fresno, Rosa; Urpi-Sarda, Mireia; Almanza-Aguilera, Enrique; Garcia-Aloy, Mar; Estruch, Ramon; Corella, Dolores; Andres-Lacueva, Cristina

2017-04-07

Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1 H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.

The future of metabolomics in ELIXIR

PubMed Central

van Rijswijk, Merlijn; Beirnaert, Charlie; Caron, Christophe; Cascante, Marta; Dominguez, Victoria; Dunn, Warwick B.; Ebbels, Timothy M. D.; Giacomoni, Franck; Gonzalez-Beltran, Alejandra; Hankemeier, Thomas; Haug, Kenneth; Izquierdo-Garcia, Jose L.; Jimenez, Rafael C.; Jourdan, Fabien; Kale, Namrata; Klapa, Maria I.; Kohlbacher, Oliver; Koort, Kairi; Kultima, Kim; Le Corguillé, Gildas; Moreno, Pablo; Moschonas, Nicholas K.; Neumann, Steffen; O’Donovan, Claire; Reczko, Martin; Rocca-Serra, Philippe; Rosato, Antonio; Salek, Reza M.; Sansone, Susanna-Assunta; Satagopam, Venkata; Schober, Daniel; Shimmo, Ruth; Spicer, Rachel A.; Spjuth, Ola; Thévenot, Etienne A.; Viant, Mark R.; Weber, Ralf J. M.; Willighagen, Egon L.; Zanetti, Gianluigi; Steinbeck, Christoph

2017-01-01

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases. PMID:29043062

The future of metabolomics in ELIXIR.

PubMed

van Rijswijk, Merlijn; Beirnaert, Charlie; Caron, Christophe; Cascante, Marta; Dominguez, Victoria; Dunn, Warwick B; Ebbels, Timothy M D; Giacomoni, Franck; Gonzalez-Beltran, Alejandra; Hankemeier, Thomas; Haug, Kenneth; Izquierdo-Garcia, Jose L; Jimenez, Rafael C; Jourdan, Fabien; Kale, Namrata; Klapa, Maria I; Kohlbacher, Oliver; Koort, Kairi; Kultima, Kim; Le Corguillé, Gildas; Moreno, Pablo; Moschonas, Nicholas K; Neumann, Steffen; O'Donovan, Claire; Reczko, Martin; Rocca-Serra, Philippe; Rosato, Antonio; Salek, Reza M; Sansone, Susanna-Assunta; Satagopam, Venkata; Schober, Daniel; Shimmo, Ruth; Spicer, Rachel A; Spjuth, Ola; Thévenot, Etienne A; Viant, Mark R; Weber, Ralf J M; Willighagen, Egon L; Zanetti, Gianluigi; Steinbeck, Christoph

2017-01-01

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.

Assessment of protein modifications in liver of rats under chronic treatment with paracetamol (acetaminophen) using two complementary mass spectrometry-based metabolomic approaches.

PubMed

Mast, Carole; Lyan, Bernard; Joly, Charlotte; Centeno, Delphine; Giacomoni, Franck; Martin, Jean-François; Mosoni, Laurent; Dardevet, Dominique; Pujos-Guillot, Estelle; Papet, Isabelle

2015-04-29

Liver protein can be altered under paracetamol (APAP) treatment. APAP-protein adducts and other protein modifications (oxidation/nitration, expression) play a role in hepatotoxicity induced by acute overdoses, but it is unknown whether liver protein modifications occur during long-term treatment with non-toxic doses of APAP. We quantified APAP-protein adducts and assessed other protein modifications in the liver from rats under chronic (17 days) treatment with two APAP doses (0.5% or 1% of APAP in the diet w/w). A targeted metabolomic method was validated and used to quantify APAP-protein adducts as APAP-cysteine adducts following proteolytic hydrolysis. The limit of detection was found to be 7ng APAP-cysteine/mL hydrolysate i.e. an APAP-Cys to tyrosine ratio of 0.016‰. Other protein modifications were assessed on the same protein hydrolysate by untargeted metabolomics including a new strategy to process the data and identify discriminant molecules. These two complementary mass spectrometry (MS)-based metabolic approaches enabled the assessment of a wide range of protein modifications induced by chronic treatment with APAP. APAP-protein adducts were detected even in the absence of glutathione depletion and hepatotoxicity, i.e. in the 0.5% APAP group, and increased by 218% in the 1% APAP group compared to the 0.5% APAP group. At the same time, the untargeted metabolomic method revealed a decrease in the binding of cysteine, cysteinyl-glycine and GSH to thiol groups of protein cysteine residues, an increase in the oxidation of tryptophan and proline residues and a modification in protein expression. This wide range of modifications in liver proteins occurred in rats under chronic treatment with APAP that did not induce hepatotoxicity. Copyright © 2015 Elsevier B.V. All rights reserved.

Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics.

PubMed

Guitton, Yann; Tremblay-Franco, Marie; Le Corguillé, Gildas; Martin, Jean-François; Pétéra, Mélanie; Roger-Mele, Pierrick; Delabrière, Alexis; Goulitquer, Sophie; Monsoor, Misharl; Duperier, Christophe; Canlet, Cécile; Servien, Rémi; Tardivel, Patrick; Caron, Christophe; Giacomoni, Franck; Thévenot, Etienne A

2017-12-01

Metabolomics is a key approach in modern functional genomics and systems biology. Due to the complexity of metabolomics data, the variety of experimental designs, and the multiplicity of bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues. Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI). Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice

PubMed Central

Nakamura, Toshihide; Sekiyama, Yasuyo; Kikuchi, Jun

2017-01-01

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods. PMID:28759594

Performance evaluation of tile-based Fisher Ratio analysis using a benchmark yeast metabolome dataset.

PubMed

Watson, Nathanial E; Parsons, Brendon A; Synovec, Robert E

2016-08-12

Performance of tile-based Fisher Ratio (F-ratio) data analysis, recently developed for discovery-based studies using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS), is evaluated with a metabolomics dataset that had been previously analyzed in great detail, but while taking a brute force approach. The previously analyzed data (referred to herein as the benchmark dataset) were intracellular extracts from Saccharomyces cerevisiae (yeast), either metabolizing glucose (repressed) or ethanol (derepressed), which define the two classes in the discovery-based analysis to find metabolites that are statistically different in concentration between the two classes. Beneficially, this previously analyzed dataset provides a concrete means to validate the tile-based F-ratio software. Herein, we demonstrate and validate the significant benefits of applying tile-based F-ratio analysis. The yeast metabolomics data are analyzed more rapidly in about one week versus one year for the prior studies with this dataset. Furthermore, a null distribution analysis is implemented to statistically determine an adequate F-ratio threshold, whereby the variables with F-ratio values below the threshold can be ignored as not class distinguishing, which provides the analyst with confidence when analyzing the hit table. Forty-six of the fifty-four benchmarked changing metabolites were discovered by the new methodology while consistently excluding all but one of the benchmarked nineteen false positive metabolites previously identified. Copyright © 2016 Elsevier B.V. All rights reserved.

Eicosapentaenoic acid prevents high fat diet-induced metabolic disorders: Genomic and metabolomic analyses of underlying mechanism

USDA-ARS?s Scientific Manuscript database

Previously our lab demonstrated eicosapenaenoic acid (EPA)'s ability to prevent high-fat (HF) diet-induced obesity by decreasing insulin resistance, glucose intolerance and inflammation. In the current study, we used genomic and metabolomic approaches to further investigate the molecular basis for t...

Antibiotic growth promoter-induced changes in the chicken intestine: A metabolomics analysis of virginiamycin and bacitracin methylene disalicylate

USDA-ARS?s Scientific Manuscript database

Abstract Although dietary antibiotic growth promoters have long been used to increase growth performance in commercial food animal production, the biochemical details associated with these effects remain poorly defined. A metabolomics approach was used to characterize and identify the biochemical co...

Informatics for Metabolomics.

PubMed

Kusonmano, Kanthida; Vongsangnak, Wanwipa; Chumnanpuen, Pramote

2016-01-01

Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era. This is directly influenced by the high-throughput analytical techniques, especially mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques. Continuously, the significant numbers of informatics techniques for data processing, statistical analysis, and data mining have been developed. The following tools and databases are advanced for the metabolomics society which provide the useful metabolomics information, e.g., the chemical structures, mass spectrum patterns for peak identification, metabolite profiles, biological functions, dynamic metabolite changes, and biochemical transformations of thousands of small molecules. In this chapter, we aim to introduce overall metabolomics studies from pre- to post-metabolomics era and their impact on society. Directing on post-metabolomics era, we provide a conceptual framework of informatics techniques for metabolomics and show useful examples of techniques, tools, and databases for metabolomics data analysis starting from preprocessing toward functional interpretation. Throughout the framework of informatics techniques for metabolomics provided, it can be further used as a scaffold for translational biomedical research which can thus lead to reveal new metabolite biomarkers, potential metabolic targets, or key metabolic pathways for future disease therapy.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

Differentiation of Panax quinquefolius grown in United States and China using LC/MS-based chromatographic fingerprinting and metabolomic approaches

USDA-ARS?s Scientific Manuscript database

American ginseng (Panax quinquefolius) is one of the most commonly used herbal medicines in the world. Discriminating between P. quinquefolius grown in different countries is difficult using the traditional quantitation methods. In this study, a liquid chromatographic mass spectrometry (LC-MS) fing...

Establishing and performing targeted multi-residue analysis for lipid mediators and fatty acids in small clinical plasma samples.

USDA-ARS?s Scientific Manuscript database

LC-MS/MS and GC-MS based targeted metabolomics is typically conducted by analyzing and quantifying a cascade of metabolites with methods specifically developed for the metabolite class. Here we describe an approach for the development of multi-residue analytical profiles, calibration standards, and ...

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes.

PubMed

Pey, Jon; Rubio, Angel; Theodoropoulos, Constantinos; Cascante, Marta; Planes, Francisco J

2012-07-01

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

Metabolic Disturbances in Adult-Onset Still's Disease Evaluated Using Liquid Chromatography/Mass Spectrometry-Based Metabolomic Analysis.

PubMed

Chen, Der-Yuan; Chen, Yi-Ming; Chien, Han-Ju; Lin, Chi-Chen; Hsieh, Chia-Wei; Chen, Hsin-Hua; Hung, Wei-Ting; Lai, Chien-Chen

2016-01-01

Liquid chromatography/mass spectrometry (LC/MS)-based comprehensive analysis of metabolic profiles with metabolomics approach has potential diagnostic and predictive implications. However, no metabolomics data have been reported in adult-onset Still's disease (AOSD). This study investigated the metabolomic profiles in AOSD patients and examined their association with clinical characteristics and disease outcome. Serum metabolite profiles were determined on 32 AOSD patients and 30 healthy controls (HC) using ultra-performance liquid chromatography (UPLC)/MS analysis, and the differentially expressed metabolites were quantified using multiple reactions monitoring (MRM)/MS analysis in 44 patients and 42 HC. Pure standards were utilized to confirm the presence of the differentially expressed metabolites. Eighteen differentially expressed metabolites were identified in AOSD patents using LC/MS-based analysis, of which 13 metabolites were validated by MRM/MS analysis. Among them, serum levels of lysoPC(18:2), urocanic acid and indole were significantly lower, and L-phenylalanine levels were significantly higher in AOSD patients compared with HC. Moreover, serum levels of lysoPC(18:2), PhePhe, uridine, taurine, L-threonine, and (R)-3-Hydroxy-hexadecanoic acid were significantly correlated with disease activity scores (all p<0.05) in AOSD patients. A different clustering of metabolites was associated with a different disease outcome, with significantly lower levels of isovalerylsarcosine observed in patients with chronic articular pattern (median, 77.0AU/ml) compared with monocyclic (341.5AU/ml, p<0.01) or polycyclic systemic pattern (168.0AU/ml, p<0.05). Thirteen differentially expressed metabolites identified and validated in AOSD patients were shown to be involved in five metabolic pathways. Significant associations of metabolic profiles with disease activity and outcome of AOSD suggest their involvement in AOSD pathogenesis.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

PubMed Central

Khoomrung, Sakda; Wanichthanarak, Kwanjeera; Nookaew, Intawat; Thamsermsang, Onusa; Seubnooch, Patcharamon; Laohapand, Tawee; Akarasereenont, Pravit

2017-01-01

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed. PMID:28769804

Review: Metabolomics in the developmental origins of obesity and its cardiometabolic consequences

PubMed Central

Hivert, MF; Perng, W; Watkins, S; Newgard, CB; Kenny, LC; Kristal, BS; Patti, ME; Isganaitis, E; DeMeo, DL; Oken, E; Gillman, MW

2015-01-01

In this review, we discuss the potential role of metabolomics to enhance understanding of obesity-related developmental origins of health and disease (DOHaD). We first provide an overview of common techniques and analytical approaches to help interested investigators dive into this relatively novel field. Next, we describe how metabolomics may capture exposures that are notoriously difficult to quantify, and help to further refine phenotypes associated with excess adiposity and related metabolic sequelae over the life course. Together, these data can ultimately help to elucidate mechanisms that underlie fetal metabolic programming. Finally, we review current gaps in knowledge and identify areas where the field of metabolomics is likely to provide insights into mechanisms linked to DOHaD in human populations. PMID:25631626

An introduction to metabolomics and its potential application in veterinary science.

PubMed

Jones, Oliver A H; Cheung, Victoria L

2007-10-01

Metabolomics has been found to be applicable to a wide range of fields, including the study of gene function, toxicology, plant sciences, environmental analysis, clinical diagnostics, nutrition, and the discrimination of organism genotypes. This approach combines high-throughput sample analysis with computer-assisted multivariate pattern-recognition techniques. It is increasingly being deployed in toxico- and pharmacokinetic studies in the pharmaceutical industry, especially during the safety assessment of candidate drugs in human medicine. However, despite the potential of this technique to reduce both costs and the numbers of animals used for research, examples of the application of metabolomics in veterinary research are, thus far, rare. Here we give an introduction to metabolomics and discuss its potential in the field of veterinary science.

Formation of dehydroalanine from mimosine and cysteine: artifacts in gas chromatography/mass spectrometry based metabolomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Young-Mo; Metz, Thomas O.; Hu, Zeping

2011-08-15

Trimethylsilyation is a chemical derivatization procedure routinely applied in gas chromatography-mass spectrometry (GC-MS)-based metabolomics. In this report, through de novo structural elucidation and comparison with authentic standards, we demonstrate that mimosine can be completely converted into dehydroalanine and 3,4-dihydroxypyridine during the trimethylsilyating process. Similarly, dehydroalanine can be formed from derivatization of cysteine. This conversion is a potential interference in GC-MS-based global metabolomics, as well as in analysis of amino acids.

Linking gene regulation and the exo-metabolome: A comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast

PubMed Central

Rossouw, Debra; Næs, Tormod; Bauer, Florian F

2008-01-01

Background 'Omics' tools provide novel opportunities for system-wide analysis of complex cellular functions. Secondary metabolism is an example of a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, is not well understood with regards to its physiological roles and genetic and biochemical regulation. Many of the metabolites produced by this network such as higher alcohols and esters are significant aroma impact compounds in fermentation products, and different yeast strains are known to produce highly divergent aroma profiles. Here, we investigated whether we can predict the impact of specific genes of known or unknown function on this metabolic network by combining whole transcriptome and partial exo-metabolome analysis. Results For this purpose, the gene expression levels of five different industrial wine yeast strains that produce divergent aroma profiles were established at three different time points of alcoholic fermentation in synthetic wine must. A matrix of gene expression data was generated and integrated with the concentrations of volatile aroma compounds measured at the same time points. This relatively unbiased approach to the study of volatile aroma compounds enabled us to identify candidate genes for aroma profile modification. Five of these genes, namely YMR210W, BAT1, AAD10, AAD14 and ACS1 were selected for overexpression in commercial wine yeast, VIN13. Analysis of the data show a statistically significant correlation between the changes in the exo-metabome of the overexpressing strains and the changes that were predicted based on the unbiased alignment of transcriptomic and exo-metabolomic data. Conclusion The data suggest that a comparative transcriptomics and metabolomics approach can be used to identify the metabolic impacts of the expression of individual genes in complex systems, and the amenability of transcriptomic data to direct applications of biotechnological relevance. PMID:18990252

MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity

PubMed Central

2012-01-01

Background Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites. Results We present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development. Conclusions MetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed at http://metamapp.fiehnlab.ucdavis.edu. PMID:22591066

Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: Investigation of nicotinic acid receptor agonists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zgoda-Pols, Joanna R., E-mail: joanna.pols@merck.com; Chowdhury, Swapan; Wirth, Mark

2011-08-15

An investigative renal toxicity study using metabolomics was conducted with a potent nicotinic acid receptor (NAR) agonist, SCH 900424. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) techniques were used to identify small molecule biomarkers of acute kidney injury (AKI) that could aid in a better mechanistic understanding of SCH 900424-induced AKI in mice. The metabolomics study revealed 3-indoxyl sulfate (3IS) as a more sensitive marker of SCH 900424-induced renal toxicity than creatinine or urea. An LC-MS assay for quantitative determination of 3IS in mouse matrices was also developed. Following treatment with SCH 900424, 3IS levels were markedly increasedmore » in murine plasma and brain, thereby potentially contributing to renal- and central nervous system (CNS)-related rapid onset of toxicities. Furthermore, significant decrease in urinary excretion of 3IS in those animals due to compromised renal function may be associated with the elevation of 3IS in plasma and brain. These data suggest that 3IS has a potential to be a marker of renal and CNS toxicities during chemically-induced AKI in mice. In addition, based on the metabolomic analysis other statistically significant plasma markers including p-cresol-sulfate and tryptophan catabolites (kynurenate, kynurenine, 3-indole-lactate) might be of toxicological importance but have not been studied in detail. This comprehensive approach that includes untargeted metabolomic and targeted bioanalytical sample analyses could be used to investigate toxicity of other compounds that pose preclinical or clinical development challenges in a pharmaceutical discovery and development. - Research Highlights: > Nicotinic acid receptor agonist, SCH 900424, caused acute kidney injury in mice. > MS-based metabolomics was conducted to identify potential small molecule markers of renal toxicity. > 3-indoxyl-sulfate was found to be as a more sensitive marker of renal toxicity than creatinine or urea. > 3-IS levels were increased not only in murine plasma but also in the brain. > 3-IS potentially contributes to renal-and CNS-related rapid onset of toxicities.« less

Orange juice affects acylcarnitine metabolism in healthy volunteers as revealed by a mass-spectrometry based metabolomics approach.

PubMed

Moreira, Vanessa; Brasili, Elisa; Fiamoncini, Jarlei; Marini, Federico; Miccheli, Alfredo; Daniel, Hannelore; Lee, Jennifer Ji Hye; Hassimotto, Neuza Mariko Aymoto; Lajolo, Franco Maria

2018-05-01

Citrus juices, especially orange juice, constitute rich sources of bioactive compounds with a wide range of health-promoting activities. Data from epidemiological and in vitro studies suggest that orange juice (OJ) may have a positive impact on lipid metabolism. However, the effect of orange juice intake on blood lipid profile is still poorly understood. We have used two different blood samples, Dried Blood Spots (DBS) and plasma, to assess the effect of two-week orange juice consumption in healthy volunteers by a mass-spectrometry based metabolomics approach. DBS were analysed by liquid chromatography mass spectrometry (LC-MS) and plasma samples were analysed by the gas chromatography mass spectrometry (GC-MS). One hundred sixty-nine lipids including acylcarnitines (AC), lysophosphatidylcholines (LysoPC), (diacyl- and acyl-alkyl-) phosphatidylcholines (PC aa and PC ae) and sphingomyelins (SM) were identified and quantified in DBS. Eighteen fatty acids were identified and quantified in plasma. Multivariate analysis allowed to identify an increase in C3:1, C5-DC(C6-OH), C5-M-DC, C5:1-DC, C8, C12-DC, lysoPC18:3, myristic acid, pentadecanoic acid, palmitoleic and palmitic acid and a decrease in nervonic acid, C0, C2, C10, C10:1, C16:1, C16-OH, C16:1-OH, C18-OH, PC aa C40:4, PC ae C38:4, PC ae C42:3, PC ae C42:4 and cholesterol levels after orange juice intake. A two-week period of orange juice intake could affect fatty acids β-oxidation through mitochondrial and peroxisomal pathways, leading to an increase of short-chain acylcarnitines and a decrease of medium and long-chain acylcarnitines. This is the first report analyzing the effect of orange juice intake in healthy volunteers using a dried blood spot-based metabolomics approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Characterizing Alzheimer's disease through metabolomics and investigating anti-Alzheimer's disease effects of natural products.

PubMed

Yi, Lunzhao; Liu, Wenbin; Wang, Zhe; Ren, Dabing; Peng, Weijun

2017-06-01

Alzheimer's disease (AD) is the most common cause of dementia in elderly people and is among the greatest healthcare challenges of the 21st century. However, the etiology and pathogenesis of AD remain poorly understood, and no curative treatments are available to slow down or stop the degenerative effects of AD. As a high-throughput approach, metabolomics is gaining significant attention in AD research, because it has a powerful potential to discover novel biomarkers, unravel new therapeutic targets for AD, and identify perturbed metabolic pathways involved in AD progression. Here, we systematically review metabolomics with regard to its recent advances and applications in the identification of potential biomarkers for early AD diagnosis and pathogenesis research. In addition, we illustrate the developments in metabolomics as an effective tool for understanding the anti-AD mechanisms of natural products. We believe that the insights from these advances can narrow the gap between metabolomics research and clinical applications of laboratory findings. Moreover, we discuss some limitations and perspectives of biomarker identification in metabolomics. © 2017 New York Academy of Sciences.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth.

PubMed

Li, Shuzhao; Dunlop, Anne L; Jones, Dean P; Corwin, Elizabeth J

2016-01-01

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science. © The Author(s) 2015.

Metabolomics of Early Stage Plant Cell–Microbe Interaction Using Stable Isotope Labeling

PubMed Central

Pang, Qiuying; Zhang, Tong; Wang, Yang; Kong, Wenwen; Guan, Qijie; Yan, Xiufeng; Chen, Sixue

2018-01-01

Metabolomics has been used in unraveling metabolites that play essential roles in plant–microbe (including pathogen) interactions. However, the problem of profiling a plant metabolome with potential contaminating metabolites from the coexisting microbes has been largely ignored. To address this problem, we implemented an effective stable isotope labeling approach, where the metabolome of a plant bacterial pathogen Pseudomonas syringae pv. tomato (Pst) DC3000 was labeled with heavy isotopes. The labeled bacterial cells were incubated with Arabidopsis thaliana epidermal peels (EPs) with guard cells, and excessive bacterial cells were subsequently removed from the plant tissues by washing. The plant metabolites were characterized by liquid chromatography mass spectrometry using multiple reactions monitoring, which can differentiate plant and bacterial metabolites. Targeted metabolomic analysis suggested that Pst DC3000 infection may modulate stomatal movement by reprograming plant signaling and primary metabolic pathways. This proof-of-concept study demonstrates the utility of this strategy in differentiation of the plant and microbe metabolomes, and it has broad applications in studying metabolic interactions between microbes and other organisms. PMID:29922325

Metabolic fingerprinting of Angelica sinensis during growth using UPLC-TOFMS and chemometrics data analysis

PubMed Central

2013-01-01

Background The radix of Angelica sinensis is widely used as a medicinal herbal and metabolomics research of this plant during growth is necessary. Results Principal component analysis of the UPLC-QTOFMS data showed that these 27 samples could be separated into 4 different groups. The chemical markers accounting for these separations were identified from the PCA loadings plot. These markers were further verified by accurate mass tandem mass and retention times of available reference standards. The study has shown that accumulation of secondary metabolites of Angelica sinensis is closely related to the growth periods. Conclusions The UPLC-QTOFMS based metabolomics approach has great potential for analysis of the alterations of secondary metabolites of Angelica sinensis during growth. PMID:23453085

Variations in gut microbiota and fecal metabolic phenotype associated with depression by 16S rRNA gene sequencing and LC/MS-based metabolomics.

PubMed

Yu, Meng; Jia, Hongmei; Zhou, Chao; Yang, Yong; Zhao, Yang; Yang, Maohua; Zou, Zhongmei

2017-05-10

As a prevalent, life-threatening and highly recurrent psychiatric illness, depression is characterized by a wide range of pathological changes; however, its etiology remains incompletely understood. Accumulating evidence supports that gut microbiota affects not only gastrointestinal physiology but also central nervous system (CNS) function and behavior through the microbiota-gut-brain axis. To assess the impact of gut microbiota on fecal metabolic phenotype in depressive conditions, an integrated approach of 16S rRNA gene sequencing combined with ultra high-performance liquid chromatography-mass spectrometry (UHPLC-MS) based metabolomics was performed in chronic variable stress (CVS)-induced depression rat model. Interestingly, depression led to significant gut microbiota changes, at the phylum and genus levels in rats treated with CVS compared to controls. The relative abundances of the bacterial genera Marvinbryantia, Corynebacterium, Psychrobacter, Christensenella, Lactobacillus, Peptostreptococcaceae incertae sedis, Anaerovorax, Clostridiales incertae sedis and Coprococcus were significantly decreased, whereas Candidatus Arthromitus and Oscillibacter were markedly increased in model rats compared with normal controls. Meanwhile, distinct changes in fecal metabolic phenotype of depressive rats were also found, including lower levels of amino acids, and fatty acids, and higher amounts of bile acids, hypoxanthine and stercobilins. Moreover, there were substantial associations of perturbed gut microbiota genera with the altered fecal metabolites, especially compounds involved in the metabolism of tryptophan and bile acids. These results showed that the gut microbiota was altered in association with fecal metabolism in depressive conditions. These findings suggest that the 16S rRNA gene sequencing and LC-MS based metabolomics approach can be further applied to assess pathogenesis of depression. Copyright © 2017 Elsevier B.V. All rights reserved.

t4 Workshop Report*

PubMed Central

Bouhifd, Mounir; Beger, Richard; Flynn, Thomas; Guo, Lining; Harris, Georgina; Hogberg, Helena; Kaddurah-Daouk, Rima; Kamp, Hennicke; Kleensang, Andre; Maertens, Alexandra; Odwin-DaCosta, Shelly; Pamies, David; Robertson, Donald; Smirnova, Lena; Sun, Jinchun; Zhao, Liang; Hartung, Thomas

2017-01-01

Summary Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however – from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining – is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics. The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools. PMID:26536290

'Systems toxicology' approach identifies coordinated metabolic responses to copper in a terrestrial non-model invertebrate, the earthworm Lumbricus rubellus.

PubMed

Bundy, Jacob G; Sidhu, Jasmin K; Rana, Faisal; Spurgeon, David J; Svendsen, Claus; Wren, Jodie F; Stürzenbaum, Stephen R; Morgan, A John; Kille, Peter

2008-06-03

New methods are needed for research into non-model organisms, to monitor the effects of toxic disruption at both the molecular and functional organism level. We exposed earthworms (Lumbricus rubellus Hoffmeister) to sub-lethal levels of copper (10-480 mg/kg soil) for 70 days as a real-world situation, and monitored both molecular (cDNA transcript microarrays and nuclear magnetic resonance-based metabolic profiling: metabolomics) and ecological/functional endpoints (reproduction rate and weight change, which have direct relevance to population-level impacts). Both of the molecular endpoints, metabolomics and transcriptomics, were highly sensitive, with clear copper-induced differences even at levels below those that caused a reduction in reproductive parameters. The microarray and metabolomic data provided evidence that the copper exposure led to a disruption of energy metabolism: transcripts of enzymes from oxidative phosphorylation were significantly over-represented, and increases in transcripts of carbohydrate metabolising enzymes (maltase-glucoamylase, mannosidase) had corresponding decreases in small-molecule metabolites (glucose, mannose). Treating both enzymes and metabolites as functional cohorts led to clear inferences about changes in energetic metabolism (carbohydrate use and oxidative phosphorylation), which would not have been possible by taking a 'biomarker' approach to data analysis. Multiple post-genomic techniques can be combined to provide mechanistic information about the toxic effects of chemical contaminants, even for non-model organisms with few additional mechanistic toxicological data. With 70-day no-observed-effect and lowest-observed-effect concentrations (NOEC and LOEC) of 10 and 40 mg kg-1 for metabolomic and microarray profiles, copper is shown to interfere with energy metabolism in an important soil organism at an ecologically and functionally relevant level.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools

PubMed Central

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K. Sreekumaran; Sumner, Susan; Subramaniam, Shankar

2016-01-01

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world. PMID:26467476

Targeted metabolomic profiling in rat tissues reveals sex differences.

PubMed

Ruoppolo, Margherita; Caterino, Marianna; Albano, Lucia; Pecce, Rita; Di Girolamo, Maria Grazia; Crisci, Daniela; Costanzo, Michele; Milella, Luigi; Franconi, Flavia; Campesi, Ilaria

2018-03-16

Sex differences affect several diseases and are organ-and parameter-specific. In humans and animals, sex differences also influence the metabolism and homeostasis of amino acids and fatty acids, which are linked to the onset of diseases. Thus, the use of targeted metabolite profiles in tissues represents a powerful approach to examine the intermediary metabolism and evidence for any sex differences. To clarify the sex-specific activities of liver, heart and kidney tissues, we used targeted metabolomics, linear discriminant analysis (LDA), principal component analysis (PCA), cluster analysis and linear correlation models to evaluate sex and organ-specific differences in amino acids, free carnitine and acylcarnitine levels in male and female Sprague-Dawley rats. Several intra-sex differences affect tissues, indicating that metabolite profiles in rat hearts, livers and kidneys are organ-dependent. Amino acids and carnitine levels in rat hearts, livers and kidneys are affected by sex: male and female hearts show the greatest sexual dimorphism, both qualitatively and quantitatively. Finally, multivariate analysis confirmed the influence of sex on the metabolomics profiling. Our data demonstrate that the metabolomics approach together with a multivariate approach can capture the dynamics of physiological and pathological states, which are essential for explaining the basis of the sex differences observed in physiological and pathological conditions.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Automated LC-HRMS(/MS) Approach for the Annotation of Fragment Ions Derived from Stable Isotope Labeling-Assisted Untargeted Metabolomics

PubMed Central

2014-01-01

Structure elucidation of biological compounds is still a major bottleneck of untargeted LC-HRMS approaches in metabolomics research. The aim of the present study was to combine stable isotope labeling and tandem mass spectrometry for the automated interpretation of the elemental composition of fragment ions and thereby facilitate the structural characterization of metabolites. The software tool FragExtract was developed and evaluated with LC-HRMS/MS spectra of both native 12C- and uniformly 13C (U-13C)-labeled analytical standards of 10 fungal substances in pure solvent and spiked into fungal culture filtrate of Fusarium graminearum respectively. Furthermore, the developed approach is exemplified with nine unknown biochemical compounds contained in F. graminearum samples derived from an untargeted metabolomics experiment. The mass difference between the corresponding fragment ions present in the MS/MS spectra of the native and U-13C-labeled compound enabled the assignment of the number of carbon atoms to each fragment signal and allowed the generation of meaningful putative molecular formulas for each fragment ion, which in turn also helped determine the elemental composition of the precursor ion. Compared to laborious manual analysis of the MS/MS spectra, the presented algorithm marks an important step toward efficient fragment signal elucidation and structure annotation of metabolites in future untargeted metabolomics studies. Moreover, as demonstrated for a fungal culture sample, FragExtract also assists the characterization of unknown metabolites, which are not contained in databases, and thus exhibits a significant contribution to untargeted metabolomics research. PMID:24965664

Metabolomics for Biomarker Discovery: Moving to the Clinic

PubMed Central

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2015-01-01

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

Metabolomic Insight into Lipid and Protein Profile in Diabetes Using Mass Spectrometry.

PubMed

Bukowiecka-Matusiak, Malgorzata; Chmielewska-Kassassir, Malgorzata; Szczesna, Dorota; Wozniak, Lucyna A

2016-01-01

In recent years, metabolomics has become a necessary tool for understanding the impact of external and pathological factors on the operation of biological systems. The first reports of metabolomics date back to the 1970s, however, the area only began to develop dynamically at the beginning of this century and has proved effective only during the present decade. The five primary tools used in this form of analysis are NMR spectrometry, HPLC, TLC-UV, GC-MS and LC-MS/MS, with MS as the most universal approach, particularly when used together with chromatographic separation and NMR. Diabetes mellitus type 2 (T2DM) is a rapidly growing problem with global consequences. The metabolomic approach has been extensively applied to examining T2DM, insulin resistance and obesity, not only to assess the development of the disease, but also to discover its potential biomarkers. The presented review summarizes current studies on lipidomic and proteomic profiles in the context of different types of diabetes mellitus disease (T1DM, T2DM and GDM), as determined by chromatography-coupled mass spectrometry.

MetaboLights: towards a new COSMOS of metabolomics data management.

PubMed

Steinbeck, Christoph; Conesa, Pablo; Haug, Kenneth; Mahendraker, Tejasvi; Williams, Mark; Maguire, Eamonn; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Salek, Reza M; Griffin, Julian L

2012-10-01

Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6-8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.

Metabolomics: A Primer.

PubMed

Liu, Xiaojing; Locasale, Jason W

2017-04-01

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future. Copyright © 2017 Elsevier Ltd. All rights reserved.

Taking Metabolomics to the Field: A Pilot Study in a Great Lakes Area of Concern (AOC)

EPA Science Inventory

Measurement of changes in endogenous metabolites via 1H-NMR-based metabolomics has shown great potential for assessing organisms exposed to environmental pollutants, and thus could aid the efforts of risk assessors. However, to date, the application of metabolomics to ecologi...

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics

PubMed Central

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A.; Caron, Christophe

2015-01-01

Summary: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. Availability and implementation: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). Contact: contact@workflow4metabolomics.org PMID:25527831

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

PubMed

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A; Caron, Christophe

2015-05-01

The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). contact@workflow4metabolomics.org. © The Author 2014. Published by Oxford University Press.

Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) D.A. Webb) skin polyphenols.

PubMed

Llorach, Rafael; Garrido, Ignacio; Monagas, Maria; Urpi-Sarda, Mireia; Tulipani, Sara; Bartolome, Begona; Andres-Lacueva, Cristina

2010-11-05

Almond, as a part of the nut family, is an important source of biological compounds, and specifically, almond skins have been considered an important source of polyphenols, including flavan-3-ols and flavonols. Polyphenol metabolism may produce several classes of metabolites that could often be more biologically active than their dietary precursor and could also become a robust new biomarker of almond polyphenol intake. In order to study urinary metabolome modifications during the 24 h after a single dose of almond skin extract, 24 volunteers (n = 24), who followed a polyphenol-free diet for 48 h before and during the study, ingested a dietary supplement of almond skin phenolic compounds (n = 12) or a placebo (n = 12). Urine samples were collected before ((-2)-0 h) and after (0-2 h, 2-6 h, 6-10 h, and 10-24 h) the intake and were analyzed by liquid chromatography-mass spectrometry (LC-q-TOF) and multivariate statistical analysis (principal component analysis (PCA) and orthogonal projection to latent structures (OPLS)). Putative identification of relevant biomarkers revealed a total of 34 metabolites associated with the single dose of almond extract, including host and, in particular, microbiota metabolites. As far as we know, this is the first time that conjugates of hydroxyphenylvaleric, hydroxyphenylpropionic, and hydroxyphenylacetic acids have been identified in human samples after the consumption of flavan-3-ols through a metabolomic approach. The results showed that this non-targeted approach could provide new intake biomarkers, contributing to the development of the food metabolome as an important part of the human urinary metabolome.

Delving deeper into technological innovations to understand differences in rice quality.

PubMed

Calingacion, Mariafe; Fang, Lu; Quiatchon-Baeza, Lenie; Mumm, Roland; Riedel, Arthur; Hall, Robert D; Fitzgerald, Melissa

2015-12-01

Increasing demand for better quality rice varieties, which are also more suited to growth under sub-optimal cultivation conditions, is driving innovation in rice research. Here we have used a multi-disciplinary approach, involving SNP-based genotyping together with phenotyping based on yield analysis, metabolomic analysis of grain volatiles, and sensory panel analysis to determine differences between two contrasting rice varieties, Apo and IR64. Plants were grown under standard and drought-induced conditions. Results revealed important differences between the volatile profiles of the two rice varieties and we relate these differences to those perceived by the sensory panel. Apo, which is the more drought tolerant variety, was less affected by the drought condition concerning both sensory profile and yield; IR64, which has higher quality but is drought sensitive, showed greater differences in these characteristics in response to the two growth conditions. Metabolomics analyses using GCxGC-MS, followed by multivariate statistical analyses of the data, revealed a number of discriminatory compounds between the varieties, but also effects of the difference in cultivation conditions. Results indicate the complexity of rice volatile profile, even of non-aromatic varieties, and how metabolomics can be used to help link changes in aroma profile with the sensory phenotype. Our outcomes also suggest valuable multi-disciplinary approaches which can be used to help define the aroma profile in rice, and its underlying genetic background, in order to support breeders in the generation of improved rice varieties combining high yield with high quality, and tolerance of both these traits to climate change.

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

PubMed

Savolainen, Otto; Fagerberg, Björn; Vendelbo Lind, Mads; Sandberg, Ann-Sofie; Ross, Alastair B; Bergström, Göran

2017-01-01

The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711]). Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

Biomarkers for predicting type 2 diabetes development—Can metabolomics improve on existing biomarkers?

PubMed Central

Savolainen, Otto; Fagerberg, Björn; Vendelbo Lind, Mads; Sandberg, Ann-Sofie; Ross, Alastair B.; Bergström, Göran

2017-01-01

Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Methods Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Results Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]). Conclusions Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model. PMID:28692646

Plasma metabolomics profiling of maintenance hemodialysis based on capillary electrophoresis - time of flight mass spectrometry.

PubMed

Liu, Shuxin; Wang, Lichao; Hu, Chunxiu; Huang, Xin; Liu, Hong; Xuan, Qiuhui; Lin, Xiaohui; Peng, Xiaojun; Lu, Xin; Chang, Ming; Xu, Guowang

2017-08-15

Uremia has been a rapidly increasing health problem in China. Hemodialysis (HD) is the main renal replacement therapy for uremia. The results of large-scale clinical trials have shown that the HD pattern is crucial for long-term prognosis of maintenance hemodialysis (MHD) in uremic patients. Plasma metabolism is very important for revealing the biological insights linked to the therapeutic effects of the HD pattern on uremia. Alteration of plasma metabolites in uremic patients in response to HD therapy has been reported. However, HD-pattern-dependent changes in plasma metabolites remain poorly understood. To this end, a capillary electrophoresis-time of flight mass spectrometry (CE-TOF/MS)-based metabolomics method was performed to systemically study the differences between HD and high flux hemodialysis (HFD) on plasma metabolite changes in patients. Three hundred and one plasma samples from three independent human cohorts (i.e., healthy controls, patients with pre-HD/post-HD, and patients with pre-HFD/post-HFD) were used in this study. Metabolites significantly changed (p < 0.05) after a single HD or HFD process. However, 11 uremic retention solutes could be more efficiently removed by HFD. Our findings indicate that a CE-TOF/MS-based metabolomics approach is promising for providing novel insights into understanding the effects of different dialysis methods on metabolite alterations of uremia.

The study on serum and urine of renal interstitial fibrosis rats induced by unilateral ureteral obstruction based on metabonomics and network analysis methods.

PubMed

Xiang, Zheng; Sun, Hao; Cai, Xiaojun; Chen, Dahui

2016-04-01

Transmission of biological information is a biochemical process of multistep cascade from genes/proteins to metabolites. However, because most metabolites reflect the terminal information of the biochemical process, it is difficult to describe the transmission process of disease information in terms of the metabolomics strategy. In this paper, by incorporating network and metabolomics methods, an integrated approach was proposed to systematically investigate and explain the molecular mechanism of renal interstitial fibrosis. Through analysis of the network, the cascade transmission process of disease information starting from genes/proteins to metabolites was putatively identified and uncovered. The results indicated that renal fibrosis was involved in metabolic pathways of glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids and arachidonic acid metabolism, riboflavin metabolism, tyrosine metabolism, and sphingolipid metabolism. These pathways involve kidney disease genes such as TGF-β1 and P2RX7. Our results showed that combining metabolomics and network analysis can provide new strategies and ideas for the interpretation of pathogenesis of disease with full consideration of "gene-protein-metabolite."

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms.

PubMed

Toya, Yoshihiro; Shimizu, Hiroshi

2013-11-01

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering. Copyright © 2013 Elsevier Inc. All rights reserved.

Metabolomic screening of regional brain alterations in the APP/PS1 transgenic model of Alzheimer's disease by direct infusion mass spectrometry.

PubMed

González-Domínguez, Raúl; García-Barrera, Tamara; Vitorica, Javier; Gómez-Ariza, José Luis

2015-01-01

The identification of pathological mechanisms underlying to Alzheimer's disease is of great importance for the discovery of potential markers for diagnosis and disease monitoring. In this study, we investigated regional metabolic alterations in brain from the APP/PS1 mice, a transgenic model that reproduces well some of the neuropathological and cognitive deficits observed in human Alzheimer's disease. For this purpose, hippocampus, cortex, cerebellum and olfactory bulbs were analyzed using a high-throughput metabolomic approach based on direct infusion mass spectrometry. Metabolic fingerprints showed significant differences between transgenic and wild-type mice in all brain tissues, being hippocampus and cortex the most affected regions. Alterations in numerous metabolites were detected including phospholipids, fatty acids, purine and pyrimidine metabolites, acylcarnitines, sterols and amino acids, among others. Furthermore, metabolic pathway analysis revealed important alterations in homeostasis of lipids, energy management, and metabolism of amino acids and nucleotides. Therefore, these findings demonstrate the potential of metabolomic screening and the use of transgenic models for understanding pathogenesis of Alzheimer's disease. Copyright © 2014 Elsevier B.V. All rights reserved.

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

PubMed Central

Luck, Margaux; Bertho, Gildas; Bateson, Mathilde; Karras, Alexandre; Yartseva, Anastasia; Thervet, Eric

2016-01-01

1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly. PMID:27861591

Fluorinated methacrylamide chitosan hydrogels enhance collagen synthesis in wound healing through increased oxygen availability.

PubMed

Patil, Pritam S; Fountas-Davis, Natalie; Huang, He; Michelle Evancho-Chapman, M; Fulton, Judith A; Shriver, Leah P; Leipzig, Nic D

2016-05-01

In this study, methacrylamide chitosan modified with perfluorocarbon chains (MACF) is used as the base material to construct hydrogel dressings for treating dermal wounds. MACF hydrogels saturated with oxygen (+O2) are examined for their ability to deliver and sustain oxygen, degrade in a biological environment, and promote wound healing in an animal model. The emerging technique of metabolomics is used to understand how MACF+O2 hydrogel dressings improve wound healing. Results indicate that MACF treatment facilitates oxygen transport rate that is two orders of magnitude greater than base MAC hydrogels. MACF hydrogel dressings are next tested in an in vivo splinted rat excisional wound healing model. Histological analysis reveals that MACF+O2 dressings improve re-epithelialization (p<0.0001) and synthesis of collagen over controls (p<0.01). Analysis of endogenous metabolites in the wounds using global metabolomics demonstrates that MACF+O2 dressings promotes a regenerative metabolic process directed toward hydroxyproline and collagen synthesis, with confirmation of metabolite levels within this pathway. The results of this study confirm that increased oxygen delivery through the application of MACF+O2 hydrogels enhances wound healing and metabolomics analyses provides a powerful tool to assess wound healing physiology. This work presents the first application of a novel class of oxygen delivering biomaterials (methacrylamide chitosan modified with perfluorocarbon chains (MACF)) as a hydrogel wound dressing. This manuscript also contains strong focus on the biochemical benefits of MACF dressings on underlying mechanisms vital to successful wound healing. In this vein, this manuscript presents the application of applied metabolomics (tandem mass spectroscopy) to uncover biomaterial interactions with wound healing mechanisms. We believe the approaches described in this manuscript will be of great interest to biomedical scientists and particularly to researchers studying wound healing, metabolomics, applied biomaterials and regenerative medicine. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Real time-based targeted Metabolomic approach as risk assessment tools for controlling superficial scald in ‘Granny Smith’ apples during storage

USDA-ARS?s Scientific Manuscript database

The risk of superficial scald incidence in ‘Granny Smith’ apple [Malus sylvestris (L.) Mill var. domestica (Borkh.) Mansf.] fruit during cold or controlled atmosphere (CA) storage increases with increased storage duration and oxygen level during CA storage. The objective of this study was to invest...

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

PubMed

Barnes, Stephen; Benton, H Paul; Casazza, Krista; Cooper, Sara J; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K; Renfrow, Matthew B; Tiwari, Hemant K

2016-07-01

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling.

PubMed

Castagné, Raphaële; Boulangé, Claire Laurence; Karaman, Ibrahim; Campanella, Gianluca; Santos Ferreira, Diana L; Kaluarachchi, Manuja R; Lehne, Benjamin; Moayyeri, Alireza; Lewis, Matthew R; Spagou, Konstantina; Dona, Anthony C; Evangelos, Vangelis; Tracy, Russell; Greenland, Philip; Lindon, John C; Herrington, David; Ebbels, Timothy M D; Elliott, Paul; Tzoulaki, Ioanna; Chadeau-Hyam, Marc

2017-10-06

1 H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1 H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

PubMed Central

2017-01-01

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies. PMID:28823158

Identifying the metabolic perturbations in earthworm induced by cypermethrin using gas chromatography-mass spectrometry based metabolomics

PubMed Central

Ch, Ratnasekhar; Singh, Amit Kumar; Pandey, Pathya; Saxena, Prem Narain; Reddy Mudiam, Mohana Krishna

2015-01-01

Globally, cypermethrin is one of the most widely used synthetic pyrethroid for agricultural and domestic purposes. Most part of the pesticides used in the agriculture ends up as residues in the soil, making soil dwelling organisms, especially earthworms more susceptible to pesticide intoxication. Cypermethrin is known to be a neurotoxicant to many model organisms, including mammals and insects, but such type of toxicity evidence is not available for invertebrate systems like earthworms. In the present work, metabolomics based approach was utilized to identify the toxic mechanism of action of cypermethrin on earthworm (Metaphire posthuma) and these were exposed to sub-lethal concentrations of cypermethrin such as 2.5 mg/kg, 5 mg/kg, 10 mg/kg and 20 mg/kg (1/40th, 1/20th, 1/10th and 1/5th of LC50, respectively) for fourteen days. The results revealed that 22 metabolites (mainly fatty acids, sugars and amino acids) were shown significant responses in the exposed earthworms and these responses are dose dependent. It is proposed that mainly carbohydrate and fatty acids in neural system metabolism was disturbed. Overall, the results provided that metabolomics can be an effective tool to understand the effects of cypermethrin on the metabolic responses of earthworm Metaphire posthuma. PMID:26514086

A Generic Multiple Reaction Monitoring Based Approach for Plant Flavonoids Profiling Using a Triple Quadrupole Linear Ion Trap Mass Spectrometry

NASA Astrophysics Data System (ADS)

Yan, Zhixiang; Lin, Ge; Ye, Yang; Wang, Yitao; Yan, Ru

2014-06-01

Flavonoids are one of the largest classes of plant secondary metabolites serving a variety of functions in plants and associating with a number of health benefits for humans. Typically, they are co-identified with many other secondary metabolites using untargeted metabolomics. The limited data quality of untargeted workflow calls for a shift from the breadth-first to the depth-first screening strategy when a specific biosynthetic pathway is focused on. Here we introduce a generic multiple reaction monitoring (MRM)-based approach for flavonoids profiling in plants using a hybrid triple quadrupole linear ion trap (QTrap) mass spectrometer. The approach includes four steps: (1) preliminary profiling of major aglycones by multiple ion monitoring triggered enhanced product ion scan (MIM-EPI); (2) glycones profiling by precursor ion triggered EPI scan (PI-EPI) of major aglycones; (3) comprehensive aglycones profiling by combining MIM-EPI and neutral loss triggered EPI scan (NL-EPI) of major glycone; (4) in-depth flavonoids profiling by MRM-EPI with elaborated MRM transitions. Particularly, incorporation of the NH3 loss and sugar elimination proved to be very informative and confirmative for flavonoids screening. This approach was applied for profiling flavonoids in Astragali radix ( Huangqi), a famous herb widely used for medicinal and nutritional purposes in China. In total, 421 flavonoids were tentatively characterized, among which less than 40 have been previously reported in this medicinal plant. This MRM-based approach provides versatility and sensitivity that required for flavonoids profiling in plants and serves as a useful tool for plant metabolomics.

A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study.

PubMed

Garcia-Aloy, Mar; Llorach, Rafael; Urpi-Sarda, Mireia; Jáuregui, Olga; Corella, Dolores; Ruiz-Canela, Miguel; Salas-Salvadó, Jordi; Fitó, Montserrat; Ros, Emilio; Estruch, Ramon; Andres-Lacueva, Cristina

2015-02-01

The aim of the current study was to apply an untargeted metabolomics strategy to characterize a model of cocoa intake biomarkers in a free-living population. An untargeted HPLC-q-ToF-MS based metabolomics approach was applied to human urine from 32 consumers of cocoa or derived products (CC) and 32 matched control subjects with no consumption of cocoa products (NC). The multivariate statistical analysis (OSC-PLS-DA) showed clear differences between CC and NC groups. The discriminant biomarkers identified were mainly related to the metabolic pathways of theobromine and polyphenols, as well as to cocoa processing. Consumption of cocoa products was also associated with reduced urinary excretions of methylglutarylcarnitine, which could be related to effects of cocoa exposure on insulin resistance. To improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. ROC curves were performed to evaluate the model and individual metabolites. The AUC values (95% CI) for the model were 95.7% (89.8-100%) and 92.6% (81.9-100%) in training and validation sets, respectively, whereas the AUCs for individual metabolites were <90%. The metabolic signature of cocoa consumption in free-living subjects reveals that combining different metabolites as biomarker models improves prediction of dietary exposure to cocoa. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

UPLC-Q-TOF-HDMS analysis of constituents in the root of two kinds of Aconitum using a metabolomics approach.

PubMed

Sun, Hui; Wang, Mo; Zhang, Aihua; Ni, Bei; Dong, Hui; Wang, Xijun

2013-01-01

Metabolomics is an 'omics' approach that aims to comprehensively analyse all metabolites in a biological sample, and has great potential for directly elucidating plant metabolic processes. Increasing evidence supports the view that plants produce a broad range of low-molecular-weight secondary metabolites responsible for variation from species to species, thus enabling the use of secondary metabolite profiling in the chemotaxonomy. To gain deeper insights into the metabolites to increasing plant diversity, we performed systematic untargeted metabolite profiling to exploit the different parts and species of Aconitum as a case study. Application of ultraperformance liquid chromatography-quadrupole time-of-flight-high-definition mass spectrometry (UPLC-QTOF-HDMS) equipped with electrospray ionisation and coupled with pattern recognition analyses to study constituents in the root of two kinds of Aconitum species. Twenty-two metabolites between the mother root of Aconitum carmichaelii Debx (CHW) and lateral root of Aconitum carmichaelii Debx (SFZ) and 13 metabolites between the CHW and root of Aconitum kusnezoffii Reichb (CW) have been identified. Of note, songorine, carmichaeline and isotalatizidine did not exist in CW, whereas they are present in the SFZ and CHW. Metabolomics based UPLC-QTOF-HDMS with multivariate statistical models was effective for analysis of constituents in the root of two kinds of Aconitum species. Copyright © 2012 John Wiley & Sons, Ltd.

Reduction in cardiometabolic risk factors by a multifunctional diet is mediated via several branches of metabolism as evidenced by nontargeted metabolite profiling approach.

PubMed

Tovar, Juscelino; de Mello, Vanessa D; Nilsson, Anne; Johansson, Maria; Paananen, Jussi; Lehtonen, Marko; Hanhineva, Kati; Björck, Inger

2017-02-01

Multifunctional diet (MFD), a diet based on multiple functional concepts and ingredients with anti-inflammatory activity, was previously shown to improve different cardiometabolic risk-associated markers in healthy subjects. Here, we assessed the impact of MFD on plasma metabolome and explored associations of the differential metabolites with clinical parameters, searching for metabolic determinants related to the effects of MFD. Forty-four overweight healthy volunteers completed a randomized crossover intervention comparing MFD with a control diet devoid of the active components of MFD. Fasting plasma samples were analyzed with nontargeted metabolite profiling at baseline and at the end (4 wk) of each diet period by LC coupled to quadrupole-TOF-MS system, revealing a vast impact of MFD on metabolic homeostasis. Main metabolite classes affected included acylcarnitines, furan fatty acids, phospholipids (plasmalogens, phosphatidylcholines, phosphatidylethanolamines), and various low-molecular weight products from the bioactivity of gut microbiota. Circulating levels of several of these metabolites correlated with changes in clinical blood lipid biomarkers. The metabolomics approach revealed that consumption of MFD affected different areas of metabolism, highlighting the impact of a healthy diet on plasma metabolome. This seems linked to reduced cardiometabolic risk and provides mechanistic insight into the effects of MFD. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of Fourier-transform ion cyclotron resonance mass spectrometry to metabolic profiling and metabolite identification.

PubMed

Ohta, Daisaku; Kanaya, Shigehiko; Suzuki, Hideyuki

2010-02-01

Metabolomics, as an essential part of genomics studies, intends holistic understanding of metabolic networks through simultaneous analysis of a myriad of both known and unknown metabolites occurring in living organisms. The initial stage of metabolomics was designed for the reproducible analyses of known metabolites based on their comparison to available authentic compounds. Such metabolomics platforms were mostly based on mass spectrometry (MS) technologies enabled by a combination of different ionization methods together with a variety of separation steps including LC, GC, and CE. Among these, Fourier-transform ion cyclotron resonance MS (FT-ICR/MS) is distinguished from other MS technologies by its ultrahigh resolution power in mass to charge ratio (m/z). The potential of FT-ICR/MS as a distinctive metabolomics tool has been demonstrated in nontargeted metabolic profiling and functional characterization of novel genes. Here, we discuss both the advantages and difficulties encountered in the FT-ICR/MS metabolomics studies.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.

PubMed

Marco-Ramell, Anna; Palau-Rodriguez, Magali; Alay, Ania; Tulipani, Sara; Urpi-Sarda, Mireia; Sanchez-Pla, Alex; Andres-Lacueva, Cristina

2018-01-02

Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Exploring the Process of Energy Generation in Pathophysiology by Targeted Metabolomics: Performance of a Simple and Quantitative Method.

PubMed

Riera-Borrull, Marta; Rodríguez-Gallego, Esther; Hernández-Aguilera, Anna; Luciano, Fedra; Ras, Rosa; Cuyàs, Elisabet; Camps, Jordi; Segura-Carretero, Antonio; Menendez, Javier A; Joven, Jorge; Fernández-Arroyo, Salvador

2016-01-01

Abnormalities in mitochondrial metabolism and regulation of energy balance contribute to human diseases. The consequences of high fat and other nutrient intake, and the resulting acquired mitochondrial dysfunction, are essential to fully understand common disorders, including obesity, cancer, and atherosclerosis. To simultaneously and noninvasively measure and quantify indirect markers of mitochondrial function, we have developed a method based on gas chromatography coupled to quadrupole-time of flight mass spectrometry and an electron ionization interface, and validated the system using plasma from patients with peripheral artery disease, human cancer cells, and mouse tissues. This approach was used to increase sensibility in the measurement of a wide dynamic range and chemical diversity of multiple intermediate metabolites used in energy metabolism. We demonstrate that our targeted metabolomics method allows for quick and accurate identification and quantification of molecules, including the measurement of small yet significant biological changes in experimental samples. The apparently low process variability required for its performance in plasma, cell lysates, and tissues allowed a rapid identification of correlations between interconnected pathways. Our results suggest that delineating the process of energy generation by targeted metabolomics can be a valid surrogate for predicting mitochondrial dysfunction in biological samples. Importantly, when used in plasma, targeted metabolomics should be viewed as a robust and noninvasive source of biomarkers in specific pathophysiological scenarios.

Exploring the Process of Energy Generation in Pathophysiology by Targeted Metabolomics: Performance of a Simple and Quantitative Method

NASA Astrophysics Data System (ADS)

Riera-Borrull, Marta; Rodríguez-Gallego, Esther; Hernández-Aguilera, Anna; Luciano, Fedra; Ras, Rosa; Cuyàs, Elisabet; Camps, Jordi; Segura-Carretero, Antonio; Menendez, Javier A.; Joven, Jorge; Fernández-Arroyo, Salvador

2016-01-01

Abnormalities in mitochondrial metabolism and regulation of energy balance contribute to human diseases. The consequences of high fat and other nutrient intake, and the resulting acquired mitochondrial dysfunction, are essential to fully understand common disorders, including obesity, cancer, and atherosclerosis. To simultaneously and noninvasively measure and quantify indirect markers of mitochondrial function, we have developed a method based on gas chromatography coupled to quadrupole-time of flight mass spectrometry and an electron ionization interface, and validated the system using plasma from patients with peripheral artery disease, human cancer cells, and mouse tissues. This approach was used to increase sensibility in the measurement of a wide dynamic range and chemical diversity of multiple intermediate metabolites used in energy metabolism. We demonstrate that our targeted metabolomics method allows for quick and accurate identification and quantification of molecules, including the measurement of small yet significant biological changes in experimental samples. The apparently low process variability required for its performance in plasma, cell lysates, and tissues allowed a rapid identification of correlations between interconnected pathways. Our results suggest that delineating the process of energy generation by targeted metabolomics can be a valid surrogate for predicting mitochondrial dysfunction in biological samples. Importantly, when used in plasma, targeted metabolomics should be viewed as a robust and noninvasive source of biomarkers in specific pathophysiological scenarios.

Plasma Metabolomics Biosignature According to HIV Stage of Infection, Pace of Disease Progression, Viremia Level and Immunological Response to Treatment.

PubMed

Scarpellini, Bruno; Zanoni, Michelle; Sucupira, Maria Cecilia Araripe; Truong, Hong-Ha M; Janini, Luiz Mario Ramos; Segurado, Ismael Dale Cotrin; Diaz, Ricardo Sobhie

2016-01-01

We evaluated plasma samples HIV-infected individuals with different phenotypic profile among five HIV-infected elite controllers and five rapid progressors after recent HIV infection and one year later and from 10 individuals subjected to antiretroviral therapy, five of whom were immunological non-responders (INR), before and after one year of antiretroviral treatment compared to 175 samples from HIV-negative patients. A targeted quantitative tandem mass spectrometry metabolomics approach was used in order to determine plasma metabolomics biosignature that may relate to HIV infection, pace of HIV disease progression, and immunological response to treatment. Twenty-five unique metabolites were identified, including five metabolites that could distinguish rapid progressors and INRs at baseline. Severe deregulation in acylcarnitine and sphingomyelin metabolism compatible with mitochondrial deficiencies was observed. β-oxidation and sphingosine-1-phosphate-phosphatase-1 activity were down-regulated, whereas acyl-alkyl-containing phosphatidylcholines and alkylglyceronephosphate synthase levels were elevated in INRs. Evidence that elite controllers harbor an inborn error of metabolism (late-onset multiple acyl-coenzyme A dehydrogenase deficiency [MADD]) was detected. Blood-based markers from metabolomics show a very high accuracy of discriminating HIV infection between varieties of controls and have the ability to predict rapid disease progression or poor antiretroviral immunological response. These metabolites can be used as biomarkers of HIV natural evolution or treatment response and provide insight into the mechanisms of the disease.

Introducing Undergraduate Students to Metabolomics Using a NMR-Based Analysis of Coffee Beans

ERIC Educational Resources Information Center

Sandusky, Peter Olaf

2017-01-01

Metabolomics applies multivariate statistical analysis to sets of high-resolution spectra taken over a population of biologically derived samples. The objective is to distinguish subpopulations within the overall sample population, and possibly also to identify biomarkers. While metabolomics has become part of the standard analytical toolbox in…

Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

NASA Astrophysics Data System (ADS)

Crutchfield, Christopher A.; Olson, Matthew T.; Gourgari, Evgenia; Nesterova, Maria; Stratakis, Constantine A.; Yergey, Alfred L.

2013-02-01

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing's disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.

Field-based Metabolomics for Assessing Contaminated Surface Waters

EPA Science Inventory

Metabolomics is becoming well-established for studying chemical contaminant-induced alterations to normal biological function. For example, the literature contains a wealth of laboratory-based studies involving analysis of samples from organisms exposed to individual chemical tox...

Metabolomic Profiling of Soybeans (Glycine Max L.) Reveals Importance of Sugar and Nitogen Metabolisms under Drought and Heat Stress

USDA-ARS?s Scientific Manuscript database

Soybean, an important legume crop, is continually threatened by abiotic stresses, especially drought and heat stress. At molecular levels, reduced yields due to drought and heat stress can be seen in the alterations of metabolic homeostasis of vegetative tissues. A global metabolomics approach can b...

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools.

PubMed

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K Sreekumaran; Sumner, Susan; Subramaniam, Shankar

2016-01-04

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

PubMed Central

Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes.

PubMed

Abu Bakar, Mohamad Hafizi; Sarmidi, Mohamad Roji; Cheng, Kian-Kai; Ali Khan, Abid; Suan, Chua Lee; Zaman Huri, Hasniza; Yaakob, Harisun

2015-07-01

Metabolomic studies on obesity and type 2 diabetes mellitus have led to a number of mechanistic insights into biomarker discovery and comprehension of disease progression at metabolic levels. This article reviews a series of metabolomic studies carried out in previous and recent years on obesity and type 2 diabetes, which have shown potential metabolic biomarkers for further evaluation of the diseases. Literature including journals and books from Web of Science, Pubmed and related databases reporting on the metabolomics in these particular disorders are reviewed. We herein discuss the potential of reported metabolic biomarkers for a novel understanding of disease processes. These biomarkers include fatty acids, TCA cycle intermediates, carbohydrates, amino acids, choline and bile acids. The biological activities and aetiological pathways of metabolites of interest in driving these intricate processes are explained. The data from various publications supported metabolomics as an effective strategy in the identification of novel biomarkers for obesity and type 2 diabetes. Accelerating interest in the perspective of metabolomics to complement other fields in systems biology towards the in-depth understanding of the molecular mechanisms underlying the diseases is also well appreciated. In conclusion, metabolomics can be used as one of the alternative approaches in biomarker discovery and the novel understanding of pathophysiological mechanisms in obesity and type 2 diabetes. It can be foreseen that there will be an increasing research interest to combine metabolomics with other omics platforms towards the establishment of detailed mechanistic evidence associated with the disease processes.

Metabolomics enables precision medicine: "A White Paper, Community Perspective".

PubMed

Beger, Richard D; Dunn, Warwick; Schmidt, Michael A; Gross, Steven S; Kirwan, Jennifer A; Cascante, Marta; Brennan, Lorraine; Wishart, David S; Oresic, Matej; Hankemeier, Thomas; Broadhurst, David I; Lane, Andrew N; Suhre, Karsten; Kastenmüller, Gabi; Sumner, Susan J; Thiele, Ines; Fiehn, Oliver; Kaddurah-Daouk, Rima

Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Targeted Metabolomics Approach To Detect the Misuse of Steroidal Aromatase Inhibitors in Equine Sports by Biomarker Profiling.

PubMed

Chan, George Ho Man; Ho, Emmie Ngai Man; Leung, David Kwan Kon; Wong, Kin Sing; Wan, Terence See Ming

2016-01-05

The use of anabolic androgenic steroids (AAS) is prohibited in both human and equine sports. The conventional approach in doping control testing for AAS (as well as other prohibited substances) is accomplished by the direct detection of target AAS or their characteristic metabolites in biological samples using hyphenated techniques such as gas chromatography or liquid chromatography coupled with mass spectrometry. Such an approach, however, falls short when dealing with unknown designer steroids where reference materials and their pharmacokinetics are not available. In addition, AASs with fast elimination times render the direct detection approach ineffective as the detection window is short. A targeted metabolomics approach is a plausible alternative to the conventional direct detection approach for controlling the misuse of AAS in sports. Because the administration of AAS of the same class may trigger similar physiological responses or effects in the body, it may be possible to detect such administrations by monitoring changes in the endogenous steroidal expression profile. This study attempts to evaluate the viability of using the targeted metabolomics approach to detect the administration of steroidal aromatase inhibitors, namely androst-4-ene-3,6,17-trione (6-OXO) and androsta-1,4,6-triene-3,17-dione (ATD), in horses. Total (free and conjugated) urinary concentrations of 31 endogenous steroids were determined by gas chromatography-tandem mass spectrometry for a group of 2 resting and 2 in-training thoroughbred geldings treated with either 6-OXO or ATD. Similar data were also obtained from a control (untreated) group of in-training thoroughbred geldings (n = 28). Statistical processing and chemometric procedures using principle component analysis and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) have highlighted 7 potential biomarkers that could be used to differentiate urine samples obtained from the control and the treated groups. On the basis of this targeted metabolomic approach, the administration of 6-OXO and ATD could be detected for much longer relative to that of the conventional direct detection approach.

Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants.

PubMed

Urpi-Sarda, M; Almanza-Aguilera, E; Llorach, R; Vázquez-Fresno, R; Estruch, R; Corella, D; Sorli, J V; Carmona, F; Sanchez-Pla, A; Salas-Salvadó, J; Andres-Lacueva, C

2018-02-20

To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D. A metabolomics analysis using the 1 H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm. A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose. The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Enzymatically Modified Starch Ameliorates Postprandial Serum Triglycerides and Lipid Metabolome in Growing Pigs.

PubMed

Metzler-Zebeli, Barbara U; Eberspächer, Eva; Grüll, Dietmar; Kowalczyk, Lidia; Molnar, Timea; Zebeli, Qendrim

2015-01-01

Developing host digestion-resistant starches to promote human health is of great research interest. Chemically modified starches (CMS) are widely used in processed foods and although the modification of the starch molecule allows specific reduction in digestibility, the metabolic effects of CMS have been less well described. This short-term study evaluated the impact of enzymatically modified starch (EMS) on fasting and postprandial profiles of blood glucose, insulin and lipids, and serum metabolome in growing pigs. Eight jugular-vein catheterized pigs (initial body weight, 37.4 kg; 4 months of age) were fed 2 diets containing 72% purified starch (EMS or waxy corn starch (control)) in a cross-over design for 7 days. On day 8, an 8-hour meal tolerance test (MTT) was performed with serial blood samplings. Besides biochemical analysis, serum was analysed for 201 metabolites through targeted mass spectrometry-based metabolomic approaches. Pigs fed the EMS diet showed increased (P<0.05) immediate serum insulin and plasma glucose response compared to pigs fed the control diet; however, area-under-the-curves for insulin and glucose were not different among diets. Results from MTT indicated reduced postprandial serum triglycerides with EMS versus control diet (P<0.05). Likewise, serum metabolome profiling identified characteristic changes in glycerophospholipid, lysophospholipids, sphingomyelins and amino acid metabolome profiles with EMS diet compared to control diet. Results showed rapid adaptations of blood metabolites to dietary starch shifts within 7 days. In conclusion, EMS ingestion showed potential to attenuate postprandial raise in serum lipids and suggested constant alteration in the synthesis or breakdown of sphingolipids and phospholipids which might be a health benefit of EMS consumption. Because serum insulin was not lowered, more research is warranted to reveal possible underlying mechanisms behind the observed changes in the profile of serum lipid metabolome in response to EMS consumption.

Enzymatically Modified Starch Ameliorates Postprandial Serum Triglycerides and Lipid Metabolome in Growing Pigs

PubMed Central

Metzler-Zebeli, Barbara U.; Eberspächer, Eva; Grüll, Dietmar; Kowalczyk, Lidia; Molnar, Timea; Zebeli, Qendrim

2015-01-01

Developing host digestion-resistant starches to promote human health is of great research interest. Chemically modified starches (CMS) are widely used in processed foods and although the modification of the starch molecule allows specific reduction in digestibility, the metabolic effects of CMS have been less well described. This short-term study evaluated the impact of enzymatically modified starch (EMS) on fasting and postprandial profiles of blood glucose, insulin and lipids, and serum metabolome in growing pigs. Eight jugular-vein catheterized pigs (initial body weight, 37.4 kg; 4 months of age) were fed 2 diets containing 72% purified starch (EMS or waxy corn starch (control)) in a cross-over design for 7 days. On day 8, an 8-hour meal tolerance test (MTT) was performed with serial blood samplings. Besides biochemical analysis, serum was analysed for 201 metabolites through targeted mass spectrometry-based metabolomic approaches. Pigs fed the EMS diet showed increased (P<0.05) immediate serum insulin and plasma glucose response compared to pigs fed the control diet; however, area-under-the-curves for insulin and glucose were not different among diets. Results from MTT indicated reduced postprandial serum triglycerides with EMS versus control diet (P<0.05). Likewise, serum metabolome profiling identified characteristic changes in glycerophospholipid, lysophospholipids, sphingomyelins and amino acid metabolome profiles with EMS diet compared to control diet. Results showed rapid adaptations of blood metabolites to dietary starch shifts within 7 days. In conclusion, EMS ingestion showed potential to attenuate postprandial raise in serum lipids and suggested constant alteration in the synthesis or breakdown of sphingolipids and phospholipids which might be a health benefit of EMS consumption. Because serum insulin was not lowered, more research is warranted to reveal possible underlying mechanisms behind the observed changes in the profile of serum lipid metabolome in response to EMS consumption. PMID:26076487

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

PubMed Central

2011-01-01

Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. Results We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals. Conclusions Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia. PMID:21575198

Clinical Metabolomics and Glaucoma.

PubMed

Barbosa-Breda, João; Himmelreich, Uwe; Ghesquière, Bart; Rocha-Sousa, Amândio; Stalmans, Ingeborg

2018-01-01

Glaucoma is one of the leading causes of irreversible blindness worldwide. However, there are no biomarkers that accurately help clinicians perform an early diagnosis or detect patients with a high risk of progression. Metabolomics is the study of all metabolites in an organism, and it has the potential to provide a biomarker. This review summarizes the findings of metabolomics in glaucoma patients and explains why this field is promising for new research. We identified published studies that focused on metabolomics and ophthalmology. After providing an overview of metabolomics in ophthalmology, we focused on human glaucoma studies. Five studies have been conducted in glaucoma patients and all compared patients to healthy controls. Using mass spectrometry, significant differences were found in blood plasma in the metabolic pathways that involve palmitoylcarnitine, sphingolipids, vitamin D-related compounds, and steroid precursors. For nuclear magnetic resonance spectroscopy, a high glutamine-glutamate/creatine ratio was found in the vitreous and lateral geniculate body; no differences were detected in the optic radiations, and a lower N-acetylaspartate/choline ratio was observed in the geniculocalcarine and striate areas. Metabolomics can move glaucoma care towards a personalized approach and provide new knowledge concerning the pathophysiology of glaucoma, which can lead to new therapeutic options. © 2017 S. Karger AG, Basel.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia.

PubMed

Pérez-Rambla, Clara; Puchades-Carrasco, Leonor; García-Flores, María; Rubio-Briones, José; López-Guerrero, José Antonio; Pineda-Lucena, Antonio

2017-01-01

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results. In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH. Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1 H nuclear magnetic resonance ( 1 H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches. The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH. PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Metabolomic profiling in perinatal asphyxia: a promising new field.

PubMed

Denihan, Niamh M; Boylan, Geraldine B; Murray, Deirdre M

2015-01-01

Metabolomics, the latest "omic" technology, is defined as the comprehensive study of all low molecular weight biochemicals, "metabolites" present in an organism. As a systems biology approach, metabolomics has huge potential to progress our understanding of perinatal asphyxia and neonatal hypoxic-ischaemic encephalopathy, by uniquely detecting rapid biochemical pathway alterations in response to the hypoxic environment. The study of metabolomic biomarkers in the immediate neonatal period is not a trivial task and requires a number of specific considerations, unique to this disease and population. Recruiting a clearly defined cohort requires standardised multicentre recruitment with broad inclusion criteria and the participation of a range of multidisciplinary staff. Minimally invasive biospecimen collection is a priority for biomarker discovery. Umbilical cord blood presents an ideal medium as large volumes can be easily extracted and stored and the sample is not confounded by postnatal disease progression. Pristine biobanking and phenotyping are essential to ensure the validity of metabolomic findings. This paper provides an overview of the current state of the art in the field of metabolomics in perinatal asphyxia and neonatal hypoxic-ischaemic encephalopathy. We detail the considerations required to ensure high quality sampling and analysis, to support scientific progression in this important field.

Metabolomic Profiling in Perinatal Asphyxia: A Promising New Field

PubMed Central

Denihan, Niamh M.; Boylan, Geraldine B.; Murray, Deirdre M.

2015-01-01

Metabolomics, the latest “omic” technology, is defined as the comprehensive study of all low molecular weight biochemicals, “metabolites” present in an organism. As a systems biology approach, metabolomics has huge potential to progress our understanding of perinatal asphyxia and neonatal hypoxic-ischaemic encephalopathy, by uniquely detecting rapid biochemical pathway alterations in response to the hypoxic environment. The study of metabolomic biomarkers in the immediate neonatal period is not a trivial task and requires a number of specific considerations, unique to this disease and population. Recruiting a clearly defined cohort requires standardised multicentre recruitment with broad inclusion criteria and the participation of a range of multidisciplinary staff. Minimally invasive biospecimen collection is a priority for biomarker discovery. Umbilical cord blood presents an ideal medium as large volumes can be easily extracted and stored and the sample is not confounded by postnatal disease progression. Pristine biobanking and phenotyping are essential to ensure the validity of metabolomic findings. This paper provides an overview of the current state of the art in the field of metabolomics in perinatal asphyxia and neonatal hypoxic-ischaemic encephalopathy. We detail the considerations required to ensure high quality sampling and analysis, to support scientific progression in this important field. PMID:25802843

Applying Metabolomics to differentiate amphibian responses ...

EPA Pesticide Factsheets

Introduction/Objectives/Methods One of the biggest challenges in ecological risk assessment is determining the impact of multiple stressors on individual organisms and populations in ‘real world’ scenarios. Emerging ‘omic technologies, notably, metabolomics, provides an opportunity to address the uncertainties surrounding ecological risk assessment of multiple stressors. The objective of this study was to use a metabolomics biomarker approach to investigate the effect of multiple stressors on amphibian metamorphs. To this end, metamorphs of Rana pipiens (northern leopard frogs) were exposed to the insecticide Carbaryl (0.32 μg/L), a conspecific predator alarm call (Lithobates catesbeianus), Carbaryl and the predator alarm call, and a control with no stressor. In addition to metabolomic fingerprinting, we measured corticosterone levels in each treatment to assess general stress response. We analyzed relative abundances of endogenous metabolites collected in liver tissue with gas chromatography coupled with mass spectrometry. Support vector machine (SVM) methods with recursive feature elimination (RFE) were applied to rank the metabolomic profiles produced. Results/Conclusions SVM-RFE of the acquired metabolomic spectra demonstrated 85-96% classification accuracy among control and all treatment groups when using the top 75 ranked retention time bins. Biochemical fluxes observed in the groups exposed to carbaryl, predation threat, and the combined treatmen

International NMR-based Environmental Metabolomics Intercomparison Exercise

EPA Science Inventory

Several fundamental requirements must be met so that NMR-based metabolomics and the related technique of metabonomics can be formally adopted into environmental monitoring and chemical risk assessment. Here we report an intercomparison exercise which has evaluated the effectivene...

Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

PubMed

Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

2017-02-01

Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

The Role of Mass Spectrometry-Based Metabolomics in Medical Countermeasures Against Radiation

PubMed Central

Patterson, Andrew D.; Lanz, Christian; Gonzalez, Frank J.; Idle, Jeffrey R.

2013-01-01

Radiation metabolomics can be defined as the global profiling of biological fluids to uncover latent, endogenous small molecules whose concentrations change in a dose-response manner following exposure to ionizing radiation. In response to the potential threat of nuclear or radiological terrorism, the Center for High-Throughput Minimally Invasive Radiation Biodosimetry (CMCR) was established to develop field-deployable biodosimeters based, in principle, on rapid analysis by mass spectrometry of readily and easily obtainable biofluids. In this review, we briefly summarize radiation biology and key events related to actual and potential nuclear disasters, discuss the important contributions the field of mass spectrometry has made to the field of radiation metabolomics, and summarize current discovery efforts to use mass spectrometry-based metabolomics to identify dose-responsive urinary constituents, and ultimately to build and deploy a noninvasive high-throughput biodosimeter. PMID:19890938

Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome.

PubMed

Naser, Fuad J; Mahieu, Nathaniel G; Wang, Lingjue; Spalding, Jonathan L; Johnson, Stephen L; Patti, Gary J

2018-02-01

Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C 8 column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C 8 RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics. Graphical abstract Analysis of semipolar and nonpolar metabolites necessitates two reversed-phase chromatography (RPLC) methods, which extend metabolome coverage more than fivefold for untargeted profiling. HILIC hydrophilic interaction liquid chromatography.

Development and validation of a highly sensitive urine-based test to identify patients with colonic adenomatous polyps.

PubMed

Wang, Haili; Tso, Victor; Wong, Clarence; Sadowski, Dan; Fedorak, Richard N

2014-03-20

Adenomatous polyps are precursors of colorectal cancer; their detection and removal is the goal of colon cancer screening programs. However, fecal-based methods identify patients with adenomatous polyps with low levels of sensitivity. The aim or this study was to develop a highly accurate, prototypic, proof-of-concept, spot urine-based diagnostic test using metabolomic technology to distinguish persons with adenomatous polyps from those without polyps. Prospective urine and stool samples were collected from 876 participants undergoing colonoscopy examination in a colon cancer screening program, from April 2008 to October 2009 at the University of Alberta. Colonoscopy reference standard identified 633 participants with no colonic polyps and 243 with colonic adenomatous polyps. One-dimensional nuclear magnetic resonance spectra of urine metabolites were analyzed to define a diagnostic metabolomic profile for colonic adenomas. A urine metabolomic diagnostic test for colonic adenomatous polyps was established using 67% of the samples (un-blinded training set) and validated using the other 33% of the samples (blinded testing set). The urine metabolomic diagnostic test's specificity and sensitivity were compared with those of fecal-based tests. Using a two-component, orthogonal, partial least-squares model of the metabolomic profile, the un-blinded training set identified patients with colonic adenomatous polyps with 88.9% sensitivity and 50.2% specificity. Validation using the blinded testing set confirmed sensitivity and specificity values of 82.7% and 51.2%, respectively. Sensitivities of fecal-based tests to identify colonic adenomas ranged from 2.5 to 11.9%. We describe a proof-of-concept spot urine-based metabolomic diagnostic test that identifies patients with colonic adenomatous polyps with a greater level of sensitivity (83%) than fecal-based tests.

Association between the metabolome and low bone mineral density in Taiwanese women determined by (1)H NMR spectroscopy.

PubMed

You, Ying-Shu; Lin, Ching-Yu; Liang, Hao-Jan; Lee, Shen-Hung; Tsai, Keh-Sung; Chiou, Jeng-Min; Chen, Yen-Ching; Tsao, Chwen-Keng; Chen, Jen-Hau

2014-01-01

Osteoporosis is related to the alteration of specific circulating metabolites. However, previous studies on only a few metabolites inadequately explain the pathogenesis of this complex syndrome. To date, no study has related the metabolome to bone mineral density (BMD), which would provide an overview of metabolism status and may be useful in clinical practice. This cross-sectional study involved 601 healthy Taiwanese women aged 40 to 55 years recruited from MJ Health Management Institution between 2009 and 2010. Participants were classified according to high (2nd tertile plus 3rd tertile) and low (1st tertile) BMD groups. The plasma metabolome was evaluated by proton nuclear magnetic resonance spectroscopy ((1) H NMR). Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and logistic regression analysis were used to assess the association between the metabolome and BMD. The high and low BMD groups could be differentiated by PLS-DA but not PCA in postmenopausal women (Q(2)  = 0.05, ppermutation  = 0.04). Among postmenopausal women, elevated glutamine was significantly associated with low BMD (adjusted odds ratio [AOR] = 5.10); meanwhile, elevated lactate (AOR = 0.55), acetone (AOR = 0.51), lipids (AOR = 0.04), and very low-density lipoprotein (AOR = 0.49) protected against low BMD. To the best of our knowledge, this study is the first to identify a group of metabolites for characterizing low BMD in postmenopausal women using a (1) H NMR-based metabolomic approach. The metabolic profile may be useful for predicting the risk of osteoporosis in postmenopausal women at an early age. © 2014 American Society for Bone and Mineral Research.

Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow.

PubMed

Kirwan, J A; Broadhurst, D I; Davidson, R L; Viant, M R

2013-06-01

Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate samples, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9%, measured as the median relative standard deviation (RSD) for the technical replicates of the biological samples, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20% as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.

A metabolomics-based approach identifies changes in the small molecular weight compound composition of the peanut as a result of dry-roasting

USDA-ARS?s Scientific Manuscript database

Peanut production in the United States is a $1.12 billion industry. United States peanut production is primarily focused on the edibles market. Peanut seeds are included in a variety of snack and confectionary products. Raw peanuts are rarely consumed in the United States. Instead, peanut seeds are ...

Chemicalome and metabolome profiling of polymethoxylated flavonoids in Citri Reticulatae Pericarpium based on an integrated strategy combining background subtraction and modified mass defect filter in a Microsoft Excel Platform.

PubMed

Zeng, Su-Ling; Duan, Li; Chen, Bai-Zhong; Li, Ping; Liu, E-Hu

2017-07-28

Detection of metabolites in complex biological matrixes is a great challenge because of the background noise and endogenous components. Herein, we proposed an integrated strategy that combined background subtraction program and modified mass defect filter (MMDF) data mining in a Microsoft Excel platform for chemicalome and metabolome profiling of the polymethoxylated flavonoids (PMFs) in Citri Reticulatae Pericarpium (CRP). The exogenously-sourced ions were firstly filtered out by the developed Visual Basic for Applications (VBA) program incorporated in the Microsoft Office. The novel MMDF strategy was proposed for detecting both target and untarget constituents and metabolites based on narrow, well-defined mass defect ranges. The approach was validated to be powerful, and potentially useful for the metabolite identification of both single compound and homologous compound mixture. We successfully identified 30 and 31 metabolites from rat biosamples after oral administration of nobiletin and tangeretin, respectively. A total of 56 PMFs compounds were chemically characterized and 125 metabolites were captured. This work demonstrated the feasibility of the integrated approach for reliable characterization of the constituents and metabolites in herbal medicines. Copyright © 2017 Elsevier B.V. All rights reserved.

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems.

PubMed

Watson, Andrew D

2006-10-01

Lipids are water-insoluble molecules that have a wide variety of functions within cells, including: 1) maintenance of electrochemical gradients; 2) subcellular partitioning; 3) first- and second-messenger cell signaling; 4) energy storage; and 5) protein trafficking and membrane anchoring. The physiological importance of lipids is illustrated by the numerous diseases to which lipid abnormalities contribute, including atherosclerosis, diabetes, obesity, and Alzheimer's disease. Lipidomics, a branch of metabolomics, is a systems-based study of all lipids, the molecules with which they interact, and their function within the cell. Recent advances in soft-ionization mass spectrometry, combined with established separation techniques, have allowed the rapid and sensitive detection of a variety of lipid species with minimal sample preparation. A "lipid profile" from a crude lipid extract is a mass spectrum of the composition and abundance of the lipids it contains, which can be used to monitor changes over time and in response to particular stimuli. Lipidomics, integrated with genomics, proteomics, and metabolomics, will contribute toward understanding how lipids function in a biological system and will provide a powerful tool for elucidating the mechanism of lipid-based disease, for biomarker screening, and for monitoring pharmacologic therapy.

A QC approach to the determination of day-to-day reproducibility and robustness of LC-MS methods for global metabolite profiling in metabonomics/metabolomics.

PubMed

Gika, Helen G; Theodoridis, Georgios A; Earll, Mark; Wilson, Ian D

2012-09-01

An approach to the determination of day-to-day analytical robustness of LC-MS-based methods for global metabolic profiling using a pooled QC sample is presented for the evaluation of metabonomic/metabolomic data. A set of 60 urine samples were repeatedly analyzed on five different days and the day-to-day reproducibility of the data obtained was determined. Multivariate statistical analysis was performed with the aim of evaluating variability and selected peaks were assessed and validated in terms of retention time stability, mass accuracy and intensity. The methodology enables the repeatability/reproducibility of extended analytical runs in large-scale studies to be determined, allowing the elimination of analytical (as opposed to biological) variability, in order to discover true patterns and correlations within the data. The day-to-day variability of the data revealed by this process suggested that, for this particular system, 3 days continuous operation was possible without the need for maintenance and cleaning. Variation was generally based on signal intensity changes over the 7-day period of the study, and was mainly a result of source contamination.

A 1H-NMR based metabolomics study of the intervention effect of mangiferin on hyperlipidemia hamsters induced by a high-fat diet.

PubMed

Guo, Fuchuan; Zi, Tianqi; Liu, Liyan; Feng, Rennan; Sun, Changhao

2017-07-19

It has been demonstrated that mangiferin can ameliorate hypertriglyceridemia by modulating the expression levels of genes involved in lipid metabolism in animal experiments, but its effects on the serum metabolic fingerprinting of hyperlipidemia animal models have not been reported. Thus, a NMR-based metabolomics approach was conducted to explore the effects of mangiferin on hyperlipidemia hamsters and to gain a better understanding of the involved metabolic pathways. Hamsters fed with a high-fat diet were orally administered with mangiferin 150 mg per kg BW once a day for 8 weeks. Serum samples were analysed by 1 H NMR, and multivariate statistical analysis was applied to the data to identify potential biomarkers. In total, 20 discriminating metabolites were identified. It turned out that mangiferin administration can partly reverse the metabolism disorders induced by a high-fat diet and exerted a good anti-hypertriglyceridemia effect. Mangiferin ameliorated hyperlipidemia by intervening in some major metabolic pathways, involving glycolysis, the TCA cycle, synthesis of ketone bodies, and BCAAs as well as choline and lipid metabolism. These findings provided new essential information on the effects of mangiferin and demonstrated the great potential of this nutrimetabolomics approach.

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

PubMed

Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

2013-08-01

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Comparison of Metabolomics Approaches for Evaluating the Variability of Complex Botanical Preparations: Green Tea (Camellia sinensis) as a Case Study.

PubMed

Kellogg, Joshua J; Graf, Tyler N; Paine, Mary F; McCune, Jeannine S; Kvalheim, Olav M; Oberlies, Nicholas H; Cech, Nadja B

2017-05-26

A challenge that must be addressed when conducting studies with complex natural products is how to evaluate their complexity and variability. Traditional methods of quantifying a single or a small range of metabolites may not capture the full chemical complexity of multiple samples. Different metabolomics approaches were evaluated to discern how they facilitated comparison of the chemical composition of commercial green tea [Camellia sinensis (L.) Kuntze] products, with the goal of capturing the variability of commercially used products and selecting representative products for in vitro or clinical evaluation. Three metabolomic-related methods-untargeted ultraperformance liquid chromatography-mass spectrometry (UPLC-MS), targeted UPLC-MS, and untargeted, quantitative 1 HNMR-were employed to characterize 34 commercially available green tea samples. Of these methods, untargeted UPLC-MS was most effective at discriminating between green tea, green tea supplement, and non-green-tea products. A method using reproduced correlation coefficients calculated from principal component analysis models was developed to quantitatively compare differences among samples. The obtained results demonstrated the utility of metabolomics employing UPLC-MS data for evaluating similarities and differences between complex botanical products.

Comparison of Metabolomics Approaches for Evaluating the Variability of Complex Botanical Preparations: Green Tea (Camellia sinensis) as a Case Study

PubMed Central

2017-01-01

A challenge that must be addressed when conducting studies with complex natural products is how to evaluate their complexity and variability. Traditional methods of quantifying a single or a small range of metabolites may not capture the full chemical complexity of multiple samples. Different metabolomics approaches were evaluated to discern how they facilitated comparison of the chemical composition of commercial green tea [Camellia sinensis (L.) Kuntze] products, with the goal of capturing the variability of commercially used products and selecting representative products for in vitro or clinical evaluation. Three metabolomic-related methods—untargeted ultraperformance liquid chromatography–mass spectrometry (UPLC-MS), targeted UPLC-MS, and untargeted, quantitative 1HNMR—were employed to characterize 34 commercially available green tea samples. Of these methods, untargeted UPLC-MS was most effective at discriminating between green tea, green tea supplement, and non-green-tea products. A method using reproduced correlation coefficients calculated from principal component analysis models was developed to quantitatively compare differences among samples. The obtained results demonstrated the utility of metabolomics employing UPLC-MS data for evaluating similarities and differences between complex botanical products. PMID:28453261

Advances in computational metabolomics and databases deepen the understanding of metabolisms.

PubMed

Tsugawa, Hiroshi

2018-01-29

Mass spectrometry (MS)-based metabolomics is the popular platform for metabolome analyses. Computational techniques for the processing of MS raw data, for example, feature detection, peak alignment, and the exclusion of false-positive peaks, have been established. The next stage of untargeted metabolomics would be to decipher the mass fragmentation of small molecules for the global identification of human-, animal-, plant-, and microbiota metabolomes, resulting in a deeper understanding of metabolisms. This review is an update on the latest computational metabolomics including known/expected structure databases, chemical ontology classifications, and mass spectrometry cheminformatics for the interpretation of mass fragmentations and for the elucidation of unknown metabolites. The importance of metabolome 'databases' and 'repositories' is also discussed because novel biological discoveries are often attributable to the accumulation of data, to relational databases, and to their statistics. Lastly, a practical guide for metabolite annotations is presented as the summary of this review. Copyright © 2018 Elsevier Ltd. All rights reserved.

MetaboLights: An Open-Access Database Repository for Metabolomics Data.

PubMed

Kale, Namrata S; Haug, Kenneth; Conesa, Pablo; Jayseelan, Kalaivani; Moreno, Pablo; Rocca-Serra, Philippe; Nainala, Venkata Chandrasekhar; Spicer, Rachel A; Williams, Mark; Li, Xuefei; Salek, Reza M; Griffin, Julian L; Steinbeck, Christoph

2016-03-24

MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools. Copyright © 2016 John Wiley & Sons, Inc.

Cellular Metabolomics for Exposure and Toxicity Assessment

EPA Science Inventory

We have developed NMR automation and cell quench methods for cell culture-based metabolomics to study chemical exposure and toxicity. Our flow automation method is robust and free of cross contamination. The direct cell quench method is rapid and effective. Cell culture-based met...

Experimental design and reporting standards for metabolomics studies of mammalian cell lines.

PubMed

Hayton, Sarah; Maker, Garth L; Mullaney, Ian; Trengove, Robert D

2017-12-01

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Metabolomics-based evidence of the hypoglycemic effect of Ge-Gen-Jiao-Tai-Wan in type 2 diabetic rats via UHPLC-QTOF/MS analysis.

PubMed

Wang, Wenbo; Zhao, Linlin; He, Zhenyu; Wu, Ning; Li, Qiuxia; Qiu, Xinjian; Zhou, Lu; Wang, Dongsheng

2018-06-12

Ge-Gen-Jiao-Tai-Wan (GGJTW) formula, derived from traditional Chinese herbal medicine, is composed of Pueraria montana var. lobata (Willd.) Sanjappa & Pradeep (Ge-Gen in Chinese), Coptis chinensis Franch (Huang-Lian), and Cinnamomum cassia (L.) J. Presl (Rou-Gui). GGJTW is used for treatment of diabetes in China, reflecting the potent hypoglycemic effect of its ingredients. However, little is known of the hypoglycemic effect of GGJTW and the underlying metabolic mechanism. This study aimed to investigate the hypoglycemic effect of GGJTW in type 2 diabetic rats and the metabolic mechanism of action. Ultra high-performance liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS)-based metabolomics approach was used for monitoring hyperglycaemia induced by high-sugar high-fat fodder and streptozotocin (STZ), and the protective effect of GGJTW. Dynamic fasting blood glucose (FBG) levels, body weight, and biochemical parameters, including lipid levels, hepatic-renal function, and hepatic histopathology were used to confirm the hyperglycaemic toxicity and attenuation effects. An orthogonal partial least squared-discriminant analysis (OPLS-DA) approach highlighted significant differences in the metabolome of the healthy control, diabetic, and drug-treated rats. The metabolomics pathway analysis (MetPA) and Kyoto encyclopedia of genes and genomes (KEGG) database were used to investigate the underlying metabolic pathways. Metabolic profiling revealed 37 metabolites as the most potential biomarker metabolites distinguishing GGJTW-treated rats from model rats. Most of the metabolites were primarily associated with bile acid metabolism and lipid metabolism. The most critical pathway was primary bile acid biosynthesis pathway involving the up-regulation of the levels of cholic acid (CA), chenodeoxycholic acid (CDCA), taurocholic acid (TCA), glycocholic acid (GCA), taurochenodesoxycholic acid (TCDCA), and taurine. The significantly-altered metabolite levels indicated the hypoglycemic effect of GGJTW on diabetic rats and the underlying metabolic mechanism. This study will be meaningful for the clinical application of GGJTW and valuable for further exploration of the mechanism. Copyright © 2018 Elsevier B.V. All rights reserved.

Serum metabolomics strategy for understanding pharmacological effects of ShenQi pill acting on kidney yang deficiency syndrome.

PubMed

Nan, Yang; Zhou, Xiaohang; Liu, Qi; Zhang, Aihua; Guan, Yu; Lin, Shanhua; Kong, Ling; Han, Ying; Wang, Xijun

2016-07-15

Kidney yang deficiency syndrome, a diagnostic pattern in Chinese medicine, is similar with clinical features of the glucocorticoid withdrawal syndrome. The aim of this present study was to explore low molecular mass differentiating metabolites between control group and model group of kidney yang deficiency rats induced with corticosterone as well as the therapeutic effect of Shen Qi Pill, a classic traditional Chinese medicine formula for treating Kidney yang deficiency syndrome in China. This study utilized ultra-performance liquid chromatography coupled with electrospray ionization synapt quadrupole time-of-flight high definition mass spectrometry (UPLC/ESI-SYNAPT-QTOF-HDMS) to identify the underlying biomarkers for clarifying mechanism of Shen Qi Pill in treating Kidney yang deficiency syndrome based on metabolite profiling of the serum samples and in conjunction with multivariate and pathway analysis. Meanwhile, blood biochemistry assay and histopathology were examined to identify specific changes in the model group rats. Distinct changes in the pattern of metabolites were observed by UPLC-HDMS. The changes in metabolic profiling were restored to their baseline values after treatment with Shen Qi Pill according to the combined with a principal component analysis (PCA) score plots. Altogether, the current metabolomics approach based on UPLC-HDMS and orthogonal projection to latent structures discriminate analysis (OPLS-DA) demonstrated 27 ions (18 in the negative mode, 9 in the positive mode, 17 ions restored by Shen Qi Pill). These results indicated that effectiveness of Shen Qi Pill in Kidney yang deficiency syndrome rats induced a substantial change in the metabolic profiles by regulating the biomarkers and adjusting the metabolic disorder. It suggested that the metabolomics approach was a powerful approach for elucidation of pathologic changes of Chinese medicine syndrome and action mechanisms of traditional Chinese medicine. Copyright © 2015 Elsevier B.V. All rights reserved.

Review of mass spectrometry-based metabolomics in cancer research

PubMed Central

Liesenfeld, David B.; Habermann, Nina; Owen, Robert W.; Scalbert, Augustin; Ulrich, Cornelia M.

2014-01-01

Metabolomics, the systematic investigation of all metabolites present within a biological system, is used in biomarker development for many human diseases, including cancer. In this review we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including <100 patients. Standardization is required especially concerning sample preparation and data analysis. In a second part of this review, we reconstructed a metabolic network of cancer patients by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed. PMID:24096148

Urine metabolomics.

PubMed

Zhang, Aihua; Sun, Hui; Wu, Xiuhong; Wang, Xijun

2012-12-24

Metabolomics is a powerful technique for the discovery of novel biomarkers and elucidation of biochemical pathways to improve diagnosis, prognosis and therapy. An advantage of this approach is its ability to assess global metabolic profiles to enhance pathologic characterization. Urine is an ideal bio-medium for disease study because it is readily available, easily obtained and less complex than other body fluids. Ease of collection allows for serial sampling to monitor disease and therapeutic response. Because of this potential, this paper will review urine metabolomic analysis, discuss its significance in the post-genomic era and highlight the specific roles of endogenous small molecule metabolites in this emerging field. Copyright © 2012 Elsevier B.V. All rights reserved.

The potential biomarkers of drug addiction: proteomic and metabolomics challenges.

PubMed

Wang, Lv; Wu, Ning; Zhao, Tai-Yun; Li, Jin

2016-07-28

Drug addiction places a significant burden on society and individuals. Proteomics and metabolomics approaches pave the road for searching potential biomarkers to assist the diagnosis and treatment. This review summarized putative drug addiction-related biomarkers in proteomics and metabolomics studies and discussed challenges and prospects in future studies. Alterations of several hundred proteins and metabolites were reported when exposure to abused drug, which enriched in energy metabolism, oxidative stress response, protein modification and degradation, synaptic function and neurotrasmission, etc. Hsp70, peroxiredoxin-6 and α- and β-synuclein, as well as n-methylserotonin and purine metabolites, were promising as potential biomarker for drug addiction.

Ion Mobility Derived Collision Cross Sections to Support Metabolomics Applications

PubMed Central

2015-01-01

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC–TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches. PMID:24640936

Quantification of Microbial Phenotypes

PubMed Central

Martínez, Verónica S.; Krömer, Jens O.

2016-01-01

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. PMID:27941694

Rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry-based metabolomics approach to study the effects of jieduquyuziyin prescription on systemic lupus erythematosus.

PubMed

Ding, Xinghong; Hu, Jinbo; Wen, Chengping; Ding, Zhishan; Yao, Li; Fan, Yongsheng

2014-01-01

Jieduquyuziyin prescription (JP), a traditional Chinese medicine (TCM) prescription, has been widely used for the clinical treatment of systemic lupus erythematosus (SLE). However, the complex chemical constituents of JP and the multifactorial pathogenesis of SLE make research on the therapeutic mechanism of JP in SLE challenging. In this paper, a serum metabolomics approach based on rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF/MS) was employed to acquire the metabolic characteristics of serum samples obtained from mice in the SLE model group, JP-treated group, prednisone acetate (PA)-treated group and control group. The orthogonal partial least squares (OPLS) was applied to recognize metabolic patterns, and an obvious separation of groups was obtained. Thirteen metabolites, namely, phosphatidylethanolamine (PE 20:3), hepoxilin B3, lyso- phosphatidylethanolamine (lyso-PE 22:6), 12S-hydroxypentaenoic acid (12S-HEPE), traumatic acid, serotonin, platelet-activating factor (PAF), phosphatidylcholine (PC 20:5),eicosapentaenoic acid (EPA), 12(S)-hydroxyei- cosatetraenoic acid (12S-HETE), 14-hydroxy docosahexaenoic acid (14-HDOHE), lyso-phosphatidylcholine (lyso-PC 20:4), and indole acetaldehyde, were identified and characterized as differential metabolites involved in the pathogenesis of SLE. After treatment with JP, the relative content of 12(S)-HETE, PAF, 12(S)-HEPE, EPA, PE (20:3), Lyso-PE(22:6), and 14-HDOHE were effectively regulated, which suggested that the therapeutic effects of JP on SLE may involve regulating disturbances to the metabolism of unsaturated fatty acid, tryptophan and phospholipid. This research also demonstrated that metabolomics is a powerful tool for researching complex disease mechanisms and evaluating the mechanism of action of TCM.

The Human Blood Metabolome-Transcriptome Interface

PubMed Central

Schramm, Katharina; Adamski, Jerzy; Gieger, Christian; Herder, Christian; Carstensen, Maren; Peters, Annette; Rathmann, Wolfgang; Roden, Michael; Strauch, Konstantin; Suhre, Karsten; Kastenmüller, Gabi; Prokisch, Holger; Theis, Fabian J.

2015-01-01

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease. PMID:26086077

Metabolomics reveals mycoplasma contamination interferes with the metabolism of PANC-1 cells.

PubMed

Yu, Tao; Wang, Yongtao; Zhang, Huizhen; Johnson, Caroline H; Jiang, Yiming; Li, Xiangjun; Wu, Zeming; Liu, Tian; Krausz, Kristopher W; Yu, Aiming; Gonzalez, Frank J; Huang, Min; Bi, Huichang

2016-06-01

Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.

High Resolution Separations and Improved Ion Production and Transmission in Metabolomics

PubMed Central

Metz, Thomas O.; Page, Jason S.; Baker, Erin S.; Tang, Keqi; Ding, Jie; Shen, Yufeng; Smith, Richard D.

2008-01-01

The goal of metabolomics analyses is the detection and quantitation of as many sample components as reasonably possible in order to identify compounds or “features” that can be used to characterize the samples under study. When utilizing electrospray ionization to produce ions for analysis by mass spectrometry (MS), it is important that metabolome sample constituents be efficiently separated prior to ion production, in order to minimize ionization suppression and thereby extend the dynamic range of the measurement, as well as the coverage of the metabolome. Similarly, optimization of the MS inlet and interface can lead to increased measurement sensitivity. This perspective review will focus on the role of high resolution liquid chromatography (LC) separations in conjunction with improved ion production and transmission for LC-MS-based metabolomics. Additional emphasis will be placed on the compromise between metabolome coverage and sample analysis throughput. PMID:19255623

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

PubMed Central

Kirwan, Jennifer A; Weber, Ralf J M; Broadhurst, David I; Viant, Mark R

2014-01-01

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment. PMID:25977770

Application of a Smartphone Metabolomics Platform to the Authentication of Schisandra sinensis.

PubMed

Kwon, Hyuk Nam; Phan, Hong-Duc; Xu, Wen Jun; Ko, Yoon-Joo; Park, Sunghyouk

2016-05-01

Herbal medicines have been used for a long time all around the world. Since the quality of herbal preparations depends on the source of herbal materials, there has been a strong need to develop methods to correctly identify the origin of materials. To develop a smartphone metabolomics platform as a simpler and low-cost alternative for the identification of herbal material source. Schisandra sinensis extracts from Korea and China were prepared. The visible spectra of all samples were measured by a smartphone spectrometer platform. This platform included all the necessary measures built-in for the metabolomics research: data acquisition, processing, chemometric analysis and visualisation of the results. The result of the smartphone metabolomics platform was compared to that of NMR-based metabolomics, suggesting the feasibility of smartphone platform in metabolomics research. The smartphone metabolomics platform gave similar results to the NMR method, showing good separation between Korean and Chinese materials and correct predictability for all test samples. With its accuracy and advantages of affordability, user-friendliness, and portability, the smartphone metabolomics platform could be applied to the authentication of other medicinal plants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomics in Newborns.

PubMed

Noto, Antonio; Fanos, Vassilios; Dessì, Angelica

2016-01-01

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction. © 2016 Elsevier Inc. All rights reserved.

Combined metabolome and proteome analysis of the mantle tissue from Pacific oyster Crassostrea gigas exposed to elevated pCO2.

PubMed

Wei, Lei; Wang, Qing; Ning, Xuanxuan; Mu, Changkao; Wang, Chunlin; Cao, Ruiwen; Wu, Huifeng; Cong, Ming; Li, Fei; Ji, Chenglong; Zhao, Jianmin

2015-03-01

Ocean acidification (OA) has been found to affect an array of normal physiological processes in mollusks, especially posing a significant threat to the fabrication process of mollusk shell. In the current study, the impact of exposure to elevated pCO2 condition was investigated in mantle tissue of Crassostrea gigas by an integrated metabolomic and proteomic approach. Analysis of metabolome and proteome revealed that elevated pCO2 could affect energy metabolism in oyster C. gigas, marked by differentially altered ATP, succinate, MDH, PEPCK and ALDH levels. Moreover, the up-regulated calponin-2, tropomyosins and myosin light chains indicated that elevated pCO2 probably caused disturbances in cytoskeleton structure in mantle tissue of oyster C. gigas. This work demonstrated that a combination of proteomics and metabolomics could provide important insights into the effects of OA at molecular levels. Copyright © 2014 Elsevier Inc. All rights reserved.

Metabolomics: Definitions and Significance in Systems Biology.

PubMed

Klassen, Aline; Faccio, Andréa Tedesco; Canuto, Gisele André Baptista; da Cruz, Pedro Luis Rocha; Ribeiro, Henrique Caracho; Tavares, Marina Franco Maggi; Sussulini, Alessandra

2017-01-01

Nowadays, there is a growing interest in deeply understanding biological mechanisms not only at the molecular level (biological components) but also the effects of an ongoing biological process in the organism as a whole (biological functionality), as established by the concept of systems biology. Within this context, metabolomics is one of the most powerful bioanalytical strategies that allow obtaining a picture of the metabolites of an organism in the course of a biological process, being considered as a phenotyping tool. Briefly, metabolomics approach consists in identifying and determining the set of metabolites (or specific metabolites) in biological samples (tissues, cells, fluids, or organisms) under normal conditions in comparison with altered states promoted by disease, drug treatment, dietary intervention, or environmental modulation. The aim of this chapter is to review the fundamentals and definitions used in the metabolomics field, as well as to emphasize its importance in systems biology and clinical studies.

Mass Spectrometry Strategies for Clinical Metabolomics and Lipidomics in Psychiatry, Neurology, and Neuro-Oncology

PubMed Central

Wood, Paul L

2014-01-01

Metabolomics research has the potential to provide biomarkers for the detection of disease, for subtyping complex disease populations, for monitoring disease progression and therapy, and for defining new molecular targets for therapeutic intervention. These potentials are far from being realized because of a number of technical, conceptual, financial, and bioinformatics issues. Mass spectrometry provides analytical platforms that address the technical barriers to success in metabolomics research; however, the limited commercial availability of analytical and stable isotope standards has created a bottleneck for the absolute quantitation of a number of metabolites. Conceptual and financial factors contribute to the generation of statistically under-powered clinical studies, whereas bioinformatics issues result in the publication of a large number of unidentified metabolites. The path forward in this field involves targeted metabolomics analyses of large control and patient populations to define both the normal range of a defined metabolite and the potential heterogeneity (eg, bimodal) in complex patient populations. This approach requires that metabolomics research groups, in addition to developing a number of analytical platforms, build sufficient chemistry resources to supply the analytical standards required for absolute metabolite quantitation. Examples of metabolomics evaluations of sulfur amino-acid metabolism in psychiatry, neurology, and neuro-oncology and of lipidomics in neurology will be reviewed. PMID:23842599

An overview of renal metabolomics.

PubMed

Kalim, Sahir; Rhee, Eugene P

2017-01-01

The high-throughput, high-resolution phenotyping enabled by metabolomics has been applied increasingly to a variety of questions in nephrology research. This article provides an overview of current metabolomics methodologies and nomenclature, citing specific considerations in sample preparation, metabolite measurement, and data analysis that investigators should understand when examining the literature or designing a study. Furthermore, we review several notable findings that have emerged in the literature that both highlight some of the limitations of current profiling approaches, as well as outline specific strengths unique to metabolomics. More specifically, we review data on the following: (i) tryptophan metabolites and chronic kidney disease onset, illustrating the interpretation of metabolite data in the context of established biochemical pathways; (ii) trimethylamine-N-oxide and cardiovascular disease in chronic kidney disease, illustrating the integration of exogenous and endogenous inputs to the blood metabolome; and (iii) renal mitochondrial function in diabetic kidney disease and acute kidney injury, illustrating the potential for rapid translation of metabolite data for diagnostic or therapeutic aims. Finally, we review future directions, including the need to better characterize interperson and intraperson variation in the metabolome, pool existing data sets to identify the most robust signals, and capitalize on the discovery potential of emerging nontargeted methods. Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Recent advances in the application of metabolomics to Alzheimer's Disease.

PubMed

Trushina, Eugenia; Mielke, Michelle M

2014-08-01

The pathophysiological changes associated with Alzheimer's Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis. © 2013.

Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology.

PubMed

Wood, Paul L

2014-01-01

Metabolomics research has the potential to provide biomarkers for the detection of disease, for subtyping complex disease populations, for monitoring disease progression and therapy, and for defining new molecular targets for therapeutic intervention. These potentials are far from being realized because of a number of technical, conceptual, financial, and bioinformatics issues. Mass spectrometry provides analytical platforms that address the technical barriers to success in metabolomics research; however, the limited commercial availability of analytical and stable isotope standards has created a bottleneck for the absolute quantitation of a number of metabolites. Conceptual and financial factors contribute to the generation of statistically under-powered clinical studies, whereas bioinformatics issues result in the publication of a large number of unidentified metabolites. The path forward in this field involves targeted metabolomics analyses of large control and patient populations to define both the normal range of a defined metabolite and the potential heterogeneity (eg, bimodal) in complex patient populations. This approach requires that metabolomics research groups, in addition to developing a number of analytical platforms, build sufficient chemistry resources to supply the analytical standards required for absolute metabolite quantitation. Examples of metabolomics evaluations of sulfur amino-acid metabolism in psychiatry, neurology, and neuro-oncology and of lipidomics in neurology will be reviewed.

Differential metabolic profiles associated to movement behaviour of stream-resident brown trout (Salmo trutta).

PubMed

Oromi, Neus; Jové, Mariona; Pascual-Pons, Mariona; Royo, Jose Luis; Rocaspana, Rafel; Aparicio, Enric; Pamplona, Reinald; Palau, Antoni; Sanuy, Delfi; Fibla, Joan; Portero-Otin, Manuel

2017-01-01

The mechanisms that can contribute in the fish movement strategies and the associated behaviour can be complex and related to the physiology, genetic and ecology of each species. In the case of the brown trout (Salmo trutta), in recent research works, individual differences in mobility have been observed in a population living in a high mountain river reach (Pyrenees, NE Spain). The population is mostly sedentary but a small percentage of individuals exhibit a mobile behavior, mainly upstream movements. Metabolomics can reflect changes in the physiological process and can determine different profiles depending on behaviour. Here, a non-targeted metabolomics approach was used to find possible changes in the blood metabolomic profile of S. trutta related to its movement behaviour, using a minimally invasive sampling. Results showed a differentiation in the metabolomic profiles of the trouts and different level concentrations of some metabolites (e.g. cortisol) according to the home range classification (pattern of movements: sedentary or mobile). The change in metabolomic profiles can generally occur during the upstream movement and probably reflects the changes in metabolite profile from the non-mobile season to mobile season. This study reveals the contribution of the metabolomic analyses to better understand the behaviour of organisms.

Arbuscular Mycorrhizal Fungi and Plant Chemical Defence: Effects of Colonisation on Aboveground and Belowground Metabolomes.

PubMed

Hill, Elizabeth M; Robinson, Lynne A; Abdul-Sada, Ali; Vanbergen, Adam J; Hodge, Angela; Hartley, Sue E

2018-02-01

Arbuscular mycorrhizal fungal (AMF) colonisation of plant roots is one of the most ancient and widespread interactions in ecology, yet the systemic consequences for plant secondary chemistry remain unclear. We performed the first metabolomic investigation into the impact of AMF colonisation by Rhizophagus irregularis on the chemical defences, spanning above- and below-ground tissues, in its host-plant ragwort (Senecio jacobaea). We used a non-targeted metabolomics approach to profile, and where possible identify, compounds induced by AMF colonisation in both roots and shoots. Metabolomics analyses revealed that 33 compounds were significantly increased in the root tissue of AMF colonised plants, including seven blumenols, plant-derived compounds known to be associated with AMF colonisation. One of these was a novel structure conjugated with a malonyl-sugar and uronic acid moiety, hitherto an unreported combination. Such structural modifications of blumenols could be significant for their previously reported functional roles associated with the establishment and maintenance of AM colonisation. Pyrrolizidine alkaloids (PAs), key anti-herbivore defence compounds in ragwort, dominated the metabolomic profiles of root and shoot extracts. Analyses of the metabolomic profiles revealed an increase in four PAs in roots (but not shoots) of AMF colonised plants, with the potential to protect colonised plants from below-ground organisms.

New approaches for metabolomics by mass spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vertes, Akos

Small molecules constitute a large part of the world around us, including fossil and some renewable energy sources. Solar energy harvested by plants and bacteria is converted into energy rich small molecules on a massive scale. Some of the worst contaminants of the environment and compounds of interest for national security also fall in the category of small molecules. The development of large scale metabolomic analysis methods lags behind the state of the art established for genomics and proteomics. This is commonly attributed to the diversity of molecular classes included in a metabolome. Unlike nucleic acids and proteins, metabolites domore » not have standard building blocks, and, as a result, their molecular properties exhibit a wide spectrum. This impedes the development of dedicated separation and spectroscopic methods. Mass spectrometry (MS) is a strong contender in the quest for a quantitative analytical tool with extensive metabolite coverage. Although various MS-based techniques are emerging for metabolomics, many of these approaches include extensive sample preparation that make large scale studies resource intensive and slow. New ionization methods are redefining the range of analytical problems that can be solved using MS. This project developed new approaches for the direct analysis of small molecules in unprocessed samples, as well as pushed the limits of ultratrace analysis in volume limited complex samples. The projects resulted in techniques that enabled metabolomics investigations with enhanced molecular coverage, as well as the study of cellular response to stimuli on a single cell level. Effectively individual cells became reaction vessels, where we followed the response of a complex biological system to external perturbation. We established two new analytical platforms for the direct study of metabolic changes in cells and tissues following external perturbation. For this purpose we developed a novel technique, laser ablation electrospray ionization (LAESI), for metabolite profiling of functioning cells and tissues. The technique was based on microscopic sampling of biological specimens by mid-infrared laser ablation followed by electrospray ionization of the plume and MS analysis. The two main shortcomings of this technique had been limited specificity due to the lack of a separation step, and limited molecular coverage, especially for nonpolar chemical species. To improve specificity and the coverage of the metabolome, we implemented the LAESI ion source on a mass spectrometer with ion mobility separation (IMS). In this system, the gas phase ions produced by the LAESI source were first sorted according to their collisional cross sections in a mobility cell. These separated ion packets were then subjected to MS analysis. By combining the atmospheric pressure ionization with IMS, we improved the metabolite coverage. Further enhancement of the non-polar metabolite coverage resulted from the combination of laser ablation with vacuum UV irradiation of the ablation plume. Our results indicated that this new ionization modality provided improved detection for neutral and non-polar compounds. Based on rapid progress in photonics, we had introduced another novel ion source that utilized the interaction of a laser pulse with silicon nanopost arrays (NAPA). In these nanophotonic ion sources, the structural features were commensurate with the wavelength of the laser light. The enhanced interaction resulted in high ion yields. This ultrasensitive analytical platform enabled the MS analysis of single yeast cells. We extended these NAPA studies from yeast to other microorganisms, including green algae (Chlamydomonas reinhardtii) that captured energy from sunlight on a massive scale. Combining cellular perturbations, e.g., through environmental changes, with the newly developed single cell analysis methods enabled us to follow dynamic changes induced in the cells. In effect, we were able to use individual cells as a “laboratory,” and approached the long-standing goal of establishing a “lab-in-a-cell.” Model systems for these studies included cells of cyanobacteria (Anabaena), yeast (Saccharomyces cerevisiae), green algae (C. reinhardtii) and Arabidopsis thaliana.« less

Evaluation of targeted and untargeted effects-based monitoring tools to assess impacts of contaminants of emerging concern on fish in the South Platte River, CO.

PubMed

Ekman, Drew R; Keteles, Kristen; Beihoffer, Jon; Cavallin, Jenna E; Dahlin, Kenneth; Davis, John M; Jastrow, Aaron; Lazorchak, James M; Mills, Marc A; Murphy, Mark; Nguyen, David; Vajda, Alan M; Villeneuve, Daniel L; Winkelman, Dana L; Collette, Timothy W

2018-08-01

Rivers in the arid Western United States face increasing influences from anthropogenic contaminants due to population growth, urbanization, and drought. To better understand and more effectively track the impacts of these contaminants, biologically-based monitoring tools are increasingly being used to complement routine chemical monitoring. This study was initiated to assess the ability of both targeted and untargeted biologically-based monitoring tools to discriminate impacts of two adjacent wastewater treatment plants (WWTPs) on Colorado's South Platte River. A cell-based estrogen assay (in vitro, targeted) determined that water samples collected downstream of the larger of the two WWTPs displayed considerable estrogenic activity in its two separate effluent streams. Hepatic vitellogenin mRNA expression (in vivo, targeted) and NMR-based metabolomic analyses (in vivo, untargeted) from caged male fathead minnows also suggested estrogenic activity downstream of the larger WWTP, but detected significant differences in responses from its two effluent streams. The metabolomic results suggested that these differences were associated with oxidative stress levels. Finally, partial least squares regression was used to explore linkages between the metabolomics responses and the chemical contaminants that were detected at the sites. This analysis, along with univariate statistical approaches, identified significant covariance between the biological endpoints and estrone concentrations, suggesting the importance of this contaminant and recommending increased focus on its presence in the environment. These results underscore the benefits of a combined targeted and untargeted biologically-based monitoring strategy when used alongside contaminant monitoring to more effectively assess ecological impacts of exposures to complex mixtures in surface waters. Published by Elsevier Ltd.

Investigating a signature of temozolomide resistance in GBM cell lines using metabolomics.

PubMed

St-Coeur, Patrick-Denis; Poitras, Julie J; Cuperlovic-Culf, Miroslava; Touaibia, Mohamed; Morin, Pier

2015-10-01

Glioblastoma multiforme (GBM) is the most common form of malignant glioma. Current therapeutic approach to treat this malignancy involves a combination of surgery, radiotherapy and chemotherapy with temozolomide. Numerous mechanisms contributing to inherent and acquired resistance to this chemotherapeutic agent have been identified and can lead to treatment failure. This study undertook a metabolomics-based approach to characterize the metabolic profiles observed in temozolomide-sensitive and temozolomide-resistant GBM cell lines as well as in a small sub-set of primary GBM tumors. This approach was also utilized to explore the metabolic changes modulated upon cell treatment with temozolomide and lomeguatrib, an MGMT inhibitor with temozolomide-sensitizing potential. Metabolites previously explored for their potential role in chemoresistance including glucose, citrate and isocitrate demonstrated elevated levels in temozolomide-resistant GBM cells. In addition, a signature of metabolites comprising alanine, choline, creatine and phosphorylcholine was identified as up-regulated in sensitive GBM cell line across different treatments. These results present the metabolic profiles associated with temozolomide response in selected GBM models and propose interesting leads that could be leveraged for the development of therapeutic or diagnostic tools to impact temozolomide response in GBMs.

Plasma Metabolomics Biosignature According to HIV Stage of Infection, Pace of Disease Progression, Viremia Level and Immunological Response to Treatment

PubMed Central

Scarpelini, Bruno; Zanoni, Michelle; Sucupira, Maria Cecilia Araripe; Truong, Hong-Ha M.; Janini, Luiz Mario Ramos; Segurado, Ismael Dale Cotrin; Diaz, Ricardo Sobhie

2016-01-01

Background We evaluated plasma samples HIV-infected individuals with different phenotypic profile among five HIV-infected elite controllers and five rapid progressors after recent HIV infection and one year later and from 10 individuals subjected to antiretroviral therapy, five of whom were immunological non-responders (INR), before and after one year of antiretroviral treatment compared to 175 samples from HIV-negative patients. A targeted quantitative tandem mass spectrometry metabolomics approach was used in order to determine plasma metabolomics biosignature that may relate to HIV infection, pace of HIV disease progression, and immunological response to treatment. Results Twenty-five unique metabolites were identified, including five metabolites that could distinguish rapid progressors and INRs at baseline. Severe deregulation in acylcarnitine and sphingomyelin metabolism compatible with mitochondrial deficiencies was observed. β-oxidation and sphingosine‐1‐phosphate-phosphatase-1 activity were down-regulated, whereas acyl-alkyl-containing phosphatidylcholines and alkylglyceronephosphate synthase levels were elevated in INRs. Evidence that elite controllers harbor an inborn error of metabolism (late-onset multiple acyl-coenzyme A dehydrogenase deficiency [MADD]) was detected. Conclusions Blood-based markers from metabolomics show a very high accuracy of discriminating HIV infection between varieties of controls and have the ability to predict rapid disease progression or poor antiretroviral immunological response. These metabolites can be used as biomarkers of HIV natural evolution or treatment response and provide insight into the mechanisms of the disease. PMID:27941971

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

PubMed Central

Fahrmann, Johannes; Grapov, Dmitry; Yang, Jun; Hammock, Bruce; Fiehn, Oliver; Bell, Graeme I.

2015-01-01

Nonobese diabetic (NOD) mice are a commonly used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra-high-performance liquid chromatography-accurate mass quadruple time-of-flight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spectrometry-based methodologies, was used to capture metabolic alterations in the metabolome and lipidome of plasma from NOD mice progressing or not progressing to T1D. Using this multi-platform approach, we identified >1,000 circulating lipids and metabolites in male and female progressor and nonprogressor animals (n = 71). Statistical and multivariate analyses were used to identify age- and sex-independent metabolic markers, which best differentiated metabolic profiles of progressors and nonprogressors. Key T1D-associated perturbations were related with 1) increases in oxidation products glucono-δ-lactone and galactonic acid and reductions in cysteine, methionine and threonic acid, suggesting increased oxidative stress; 2) reductions in circulating polyunsaturated fatty acids and lipid signaling mediators, most notably arachidonic acid (AA) and AA-derived eicosanoids, implying impaired states of systemic inflammation; 3) elevations in circulating triacylglyercides reflective of hypertriglyceridemia; and 4) reductions in major structural lipids, most notably lysophosphatidylcholines and phosphatidylcholines. Taken together, our results highlight the systemic perturbations that accompany a loss of glycemic control and development of overt T1D. PMID:25852003

Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow.

PubMed

Chen, Gengbo; Walmsley, Scott; Cheung, Gemmy C M; Chen, Liyan; Cheng, Ching-Yu; Beuerman, Roger W; Wong, Tien Yin; Zhou, Lei; Choi, Hyungwon

2017-05-02

Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis.

Bisphenol A alters n-6 fatty acid composition and decreases antioxidant enzyme levels in rat testes: a LC-QTOF-based metabolomics study.

PubMed

Chen, Minjian; Xu, Bin; Ji, Wenliang; Qiao, Shanlei; Hu, Nan; Hu, Yanhui; Wu, Wei; Qiu, Lianglin; Zhang, Ruyang; Wang, Yubang; Wang, Shoulin; Zhou, Zuomin; Xia, Yankai; Wang, Xinru

2012-01-01

Male reproductive toxicity induced by exposure to bisphenol A (BPA) has been widely reported. The testes have proven to be a major target organ of BPA toxicity, so studying testicular metabolite variation holds promise for the discovery of mechanisms linked to the toxic effects of BPA on reproduction. Male Sprague-Dawley rats were orally administered doses of BPA at the levels of 0, 50 mg/kg/d for 8 weeks. We used an unbiased liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomics approach to discover, identify, and analyze the variation of testicular metabolites. Two n-6 fatty acids, linoleic acid (LA) and arachidonic acid (AA) were identified as potential testicular biomarkers. Decreased levels of LA and increased levels of AA as well as AA/LA ratio were observed in the testes of the exposed group. According to these suggestions, testicular antioxidant enzyme levels were detected. Testicular superoxide dismutase (SOD) declined significantly in the exposed group compared with that in the non-exposed group, and the glutathione peroxidase (GSH-Px) as well as catalase (CAT) also showed a decreasing trend in BPA treated group. BPA caused testicular n-6 fatty acid composition variation and decreased antioxidant enzyme levels. This study emphasizes that metabolomics brings the promise of biomarkers identification for the discovery of mechanisms underlying reproductive toxicity.

Cordyceps sinensis protects against liver and heart injuries in a rat model of chronic kidney disease: a metabolomic analysis.

PubMed

Liu, Xia; Zhong, Fang; Tang, Xu-long; Lian, Fu-lin; Zhou, Qiao; Guo, Shan-mai; Liu, Jia-fu; Sun, Peng; Hao, Xu; Lu, Ying; Wang, Wei-ming; Chen, Nan; Zhang, Nai-xia

2014-05-01

To test the hypothesis that the traditional Chinese medicine Cordyceps sinensis could improve the metabolic function of extrarenal organs to achieve its anti-chronic kidney disease (CKD) effects. Male SD rats were divided into CKD rats (with 5/6-nephrectomy), CKD rats treated with Cordyceps sinensis (4 mg•kg-1•d-1, po), and sham-operated rats. After an 8-week treatment, metabolites were extracted from the hearts and livers of the rats, and then subjected to (1)H-NMR-based metabolomic analysis. Oxidative stress, energy metabolism, amino acid and protein metabolism and choline metabolism were considered as links between CKD and extrarenal organ dysfunction. Within the experimental period of 8 weeks, the metabolic disorders in the liver were more pronounced than in the heart, suggesting that CKD-related extrarenal organ dysfunctions occurred sequentially rather than simultaneously. Oral administration of Cordyceps sinensis exerted statistically significant rescue effects on the liver and heart by reversely regulating levels of those metabolites that are typically perturbed in CKD. Oral administration of Cordyceps sinensis significantly attenuates the liver and heart injuries in CKD rats. The (1)H NMR-based metabolomic approach has provided a systematic view for understanding of CKD and the drug treatment, which can also be used to elucidate the mechanisms of action of other traditional Chinese medicines.

Cordyceps sinensis protects against liver and heart injuries in a rat model of chronic kidney disease: a metabolomic analysis

PubMed Central

Liu, Xia; Zhong, Fang; Tang, Xu-long; Lian, Fu-lin; Zhou, Qiao; Guo, Shan-mai; Liu, Jia-fu; Sun, Peng; Hao, Xu; Lu, Ying; Wang, Wei-ming; Chen, Nan; Zhang, Nai-xia

2014-01-01

Aim: To test the hypothesis that the traditional Chinese medicine Cordyceps sinensis could improve the metabolic function of extrarenal organs to achieve its anti-chronic kidney disease (CKD) effects. Methods: Male SD rats were divided into CKD rats (with 5/6-nephrectomy), CKD rats treated with Cordyceps sinensis (4 mg•kg-1•d-1, po), and sham-operated rats. After an 8-week treatment, metabolites were extracted from the hearts and livers of the rats, and then subjected to 1H-NMR-based metabolomic analysis. Results: Oxidative stress, energy metabolism, amino acid and protein metabolism and choline metabolism were considered as links between CKD and extrarenal organ dysfunction. Within the experimental period of 8 weeks, the metabolic disorders in the liver were more pronounced than in the heart, suggesting that CKD-related extrarenal organ dysfunctions occurred sequentially rather than simultaneously. Oral administration of Cordyceps sinensis exerted statistically significant rescue effects on the liver and heart by reversely regulating levels of those metabolites that are typically perturbed in CKD. Conclusion: Oral administration of Cordyceps sinensis significantly attenuates the liver and heart injuries in CKD rats. The 1H NMR-based metabolomic approach has provided a systematic view for understanding of CKD and the drug treatment, which can also be used to elucidate the mechanisms of action of other traditional Chinese medicines. PMID:24632844

Quantitative Metabolomics Reveals an Epigenetic Blueprint for Iron Acquisition in Uropathogenic Escherichia coli

PubMed Central

Henderson, Jeffrey P.; Crowley, Jan R.; Pinkner, Jerome S.; Walker, Jennifer N.; Tsukayama, Pablo; Stamm, Walter E.; Hooton, Thomas M.; Hultgren, Scott J.

2009-01-01

Bacterial pathogens are frequently distinguished by the presence of acquired genes associated with iron acquisition. The presence of specific siderophore receptor genes, however, does not reliably predict activity of the complex protein assemblies involved in synthesis and transport of these secondary metabolites. Here, we have developed a novel quantitative metabolomic approach based on stable isotope dilution to compare the complement of siderophores produced by Escherichia coli strains associated with intestinal colonization or urinary tract disease. Because uropathogenic E. coli are believed to reside in the gut microbiome prior to infection, we compared siderophore production between urinary and rectal isolates within individual patients with recurrent UTI. While all strains produced enterobactin, strong preferential expression of the siderophores yersiniabactin and salmochelin was observed among urinary strains. Conventional PCR genotyping of siderophore receptors was often insensitive to these differences. A linearized enterobactin siderophore was also identified as a product of strains with an active salmochelin gene cluster. These findings argue that qualitative and quantitative epi-genetic optimization occurs in the E. coli secondary metabolome among human uropathogens. Because the virulence-associated biosynthetic pathways are distinct from those associated with rectal colonization, these results suggest strategies for virulence-targeted therapies. PMID:19229321

Urinary Metabolomic Approach Provides New Insights into Distinct Metabolic Profiles of Glutamine and N-Carbamylglutamate Supplementation in Rats.

PubMed

Liu, Guangmang; Cao, Wei; Fang, Tingting; Jia, Gang; Zhao, Hua; Chen, Xiaoling; Wu, Caimei; Wang, Jing

2016-08-04

Glutamine and N-carbamylglutamate can enhance growth performance and health in animals, but the underlying mechanisms are not yet elucidated. This study aimed to investigate the effect of glutamine and N-carbamylglutamate supplementation in rat metabolism. Thirty rats were fed a control, glutamine, or N-carbamylglutamate diet for four weeks. Urine samples were analyzed by nuclear magnetic resonance (NMR)-based metabolomics, specifically high-resolution ¹H NMR metabolic profiling combined with multivariate data analysis. Glutamine significantly increased the urine levels of acetamide, acetate, citrulline, creatinine, and methymalonate, and decreased the urine levels of ethanol and formate (p < 0.05). Moreover, N-carbamylglutamate significantly increased the urine levels of creatinine, ethanol, indoxyl sulfate, lactate, methymalonate, acetoacetate, m-hydroxyphenylacetate, and sarcosine, and decreased the urine levels of acetamide, acetate, citrulline, creatine, glycine, hippurate, homogentisate, N-acetylglutamate, phenylacetyglycine, acetone, and p-hydroxyphenylacetate (p < 0.05). Results suggested that glutamine and N-carbamylglutamate could modify urinary metabolome related to nitrogen metabolism and gut microbiota metabolism. Moreover, N-carbamylglutamate could alter energy and lipid metabolism. These findings indicate that different arginine precursors may lead to differences in the biofluid profile in rats.

Urinary Metabolomic Approach Provides New Insights into Distinct Metabolic Profiles of Glutamine and N-Carbamylglutamate Supplementation in Rats

PubMed Central

Liu, Guangmang; Cao, Wei; Fang, Tingting; Jia, Gang; Zhao, Hua; Chen, Xiaoling; Wu, Caimei; Wang, Jing

2016-01-01

Glutamine and N-carbamylglutamate can enhance growth performance and health in animals, but the underlying mechanisms are not yet elucidated. This study aimed to investigate the effect of glutamine and N-carbamylglutamate supplementation in rat metabolism. Thirty rats were fed a control, glutamine, or N-carbamylglutamate diet for four weeks. Urine samples were analyzed by nuclear magnetic resonance (NMR)-based metabolomics, specifically high-resolution 1H NMR metabolic profiling combined with multivariate data analysis. Glutamine significantly increased the urine levels of acetamide, acetate, citrulline, creatinine, and methymalonate, and decreased the urine levels of ethanol and formate (p < 0.05). Moreover, N-carbamylglutamate significantly increased the urine levels of creatinine, ethanol, indoxyl sulfate, lactate, methymalonate, acetoacetate, m-hydroxyphenylacetate, and sarcosine, and decreased the urine levels of acetamide, acetate, citrulline, creatine, glycine, hippurate, homogentisate, N-acetylglutamate, phenylacetyglycine, acetone, and p-hydroxyphenylacetate (p < 0.05). Results suggested that glutamine and N-carbamylglutamate could modify urinary metabolome related to nitrogen metabolism and gut microbiota metabolism. Moreover, N-carbamylglutamate could alter energy and lipid metabolism. These findings indicate that different arginine precursors may lead to differences in the biofluid profile in rats. PMID:27527211

Effects of MeJA on Arabidopsis metabolome under endogenous JA deficiency

NASA Astrophysics Data System (ADS)

Cao, Jingjing; Li, Mengya; Chen, Jian; Liu, Pei; Li, Zhen

2016-11-01

Jasmonates (JAs) play important roles in plant growth, development and defense. Comprehensive metabolomics profiling of plants under JA treatment provides insights into the interaction and regulation network of plant hormones. Here we applied high resolution mass spectrometry based metabolomics approach on Arabidopsis wild type and JA synthesis deficiency mutant opr3. The effects of exogenous MeJA treatment on the metabolites of opr3 were investigated. More than 10000 ion signals were detected and more than 2000 signals showed significant variation in different genotypes and treatment groups. Multivariate statistic analyses (PCA and PLS-DA) were performed and a differential compound library containing 174 metabolites with high resolution precursor ion-product ions pairs was obtained. Classification and pathway analysis of 109 identified compounds in this library showed that glucosinolates and tryptophan metabolism, amino acids and small peptides metabolism, lipid metabolism, especially fatty acyls metabolism, were impacted by endogenous JA deficiency and exogenous MeJA treatment. These results were further verified by quantitative reverse transcription PCR (RT-qPCR) analysis of 21 related genes involved in the metabolism of glucosinolates, tryptophan and α-linolenic acid pathways. The results would greatly enhance our understanding of the biological functions of JA.

A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies

PubMed Central

Jobard, Elodie; Trédan, Olivier; Postoly, Déborah; André, Fabrice; Martin, Anne-Laure; Elena-Herrmann, Bénédicte; Boyault, Sandrine

2016-01-01

The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies. PMID:27929400

Metabolomic profiling of serum in the progression of Alzheimer's disease by capillary electrophoresis-mass spectrometry.

PubMed

González-Domínguez, Raúl; García, Antonia; García-Barrera, Tamara; Barbas, Coral; Gómez-Ariza, José Luis

2014-12-01

There is high interest in the discovery of early diagnostic biomarkers of Alzheimer's disease, for which metabolomics exhibits a great potential. In this work, a metabolomic approach based on ultrafiltration and analysis by CE-MS has been used to obtain representative fingerprints of polar metabolites from serum samples in order to distinguish between patients with Alzheimer's disease, mild cognitive impairment, and healthy controls. By the use of partial least squares discriminant analysis it was possible to classify patients according to the disease stage and then identify potential markers. Significant increase was observed with progression of disease in levels of choline, creatinine, asymmetric dimethyl-arginine, homocysteine-cysteine disulfide, phenylalanyl-phenylalanine, and different medium chain acylcarnitines. On the other hand, asparagine, methionine, histidine, carnitine, acetyl-spermidine, and C5-carnitine were reduced in these serum samples. In this way, multiple essential pathways were found implicated in the underlying pathology, such as oxidative stress or defects in energy metabolism. However, the most interesting results are related to the association of several vascular risk factors with Alzheimer's disease. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action.

PubMed

Xuan, Jiekun; Pan, Guihua; Qiu, Yunping; Yang, Lun; Su, Mingming; Liu, Yumin; Chen, Jian; Feng, Guoyin; Fang, Yiru; Jia, Wei; Xing, Qinghe; He, Lin

2011-12-02

Despite recent advances in understanding the pathophysiology of schizophrenia and the mechanisms of antipsychotic drug action, the development of biomarkers for diagnosis and therapeutic monitoring in schizophrenia remains challenging. Metabolomics provides a powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual's metabolic profile in response to pathophysiological stimuli or drug intervention. In this study, we performed gas chromatography-mass spectrometry based metabolomic profiling in serum of unmedicated schizophrenic patients before and after an 8-week risperidone monotherapy, to detect potential biomarkers associated with schizophrenia and risperidone treatment. Twenty-two marker metabolites contributing to the complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myo-inositol, and allantoin exhibiting the best combined classification performance. Twenty marker metabolites contributing to the complete separation between posttreatment and pretreatment patients were identified, with myo-inositol, uric acid, and tryptophan showing the maximum combined classification performance. Metabolic pathways including energy metabolism, antioxidant defense systems, neurotransmitter metabolism, fatty acid biosynthesis, and phospholipid metabolism were found to be disturbed in schizophrenic patients and partially normalized following risperidone therapy. Further study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic strategies for schizophrenia.

Influence of yeast and lactic acid bacterium on the constituent profile of soy sauce during fermentation.

PubMed

Harada, Risa; Yuzuki, Masanobu; Ito, Kotaro; Shiga, Kazuki; Bamba, Takeshi; Fukusaki, Eiichiro

2017-02-01

Soy sauce is a Japanese traditional seasoning composed of various constituents that are produced by various microbes during a long-term fermentation process. Due to the complexity of the process, the investigation of the constituent profile during fermentation is difficult. Metabolomics, the comprehensive study of low molecular weight compounds in biological samples, is thought to be a promising strategy for deep understanding of the constituent contribution to food flavor characteristics. Therefore, metabolomics is suitable for the analysis of soy sauce fermentation. Unfortunately, only few and unrefined studies of soy sauce fermentation using metabolomics approach have been reported. Therefore, we investigated changes in low molecular weight hydrophilic and volatile compounds of soy sauce using gas chromatography/mass spectrometry (GC/MS)-based non-targeted metabolic profiling. The data were analyzed by statistical analysis to evaluate influences of yeast and lactic acid bacterium on the constituent profile. Consequently, our results suggested a novel finding that lactic acid bacterium affected the production of several constituents such as cyclotene, furfural, furfuryl alcohol and methional in the soy sauce fermentation process. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes

DOE PAGES

Vetting, Matthew W.; Al-Obaidi, Nawar; Zhao, Suwen; ...

2014-12-25

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. Here in this paper, we describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of themore » library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of D-Ala-D-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.« less

Phytochemical genomics--a new trend.

PubMed

Saito, Kazuki

2013-06-01

Phytochemical genomics is a recently emerging field, which investigates the genomic basis of the synthesis and function of phytochemicals (plant metabolites), particularly based on advanced metabolomics. The chemical diversity of the model plant Arabidopsis thaliana is larger than previously expected, and the gene-to-metabolite correlations have been elucidated mostly by an integrated analysis of transcriptomes and metabolomes. For example, most genes involved in the biosynthesis of flavonoids in Arabidopsis have been characterized by this method. A similar approach has been applied to the functional genomics for production of phytochemicals in crops and medicinal plants. Great promise is seen in metabolic quantitative loci analysis in major crops such as rice and tomato, and identification of novel genes involved in the biosynthesis of bioactive specialized metabolites in medicinal plants. Copyright © 2013 The Author. Published by Elsevier Ltd.. All rights reserved.

Metabolomics for undergraduates: Identification and pathway assignment of mitochondrial metabolites.

PubMed

Marques, Ana Patrícia; Serralheiro, Maria Luisa; Ferreira, António E N; Freire, Ana Ponces; Cordeiro, Carlos; Silva, Marta Sousa

2016-01-01

Metabolomics is a key discipline in systems biology, together with genomics, transcriptomics, and proteomics. In this omics cascade, the metabolome represents the biochemical products that arise from cellular processes and is often regarded as the final response of a biological system to environmental or genetic changes. The overall screening approach to identify all the metabolites in a given biological system is called metabolic fingerprinting. Using high-resolution and high-mass accuracy mass spectrometry, large metabolome coverage, sensitivity, and specificity can be attained. Although the theoretical concepts of this methodology are usually provided in life-science programs, hands-on laboratory experiments are not usually accessible to undergraduate students. Even if the instruments are available, there are not simple laboratory protocols created specifically for teaching metabolomics. We designed a straightforward hands-on laboratory experiment to introduce students to this methodology, relating it to biochemical knowledge through metabolic pathway mapping of the identified metabolites. This study focuses on mitochondrial metabolomics since mitochondria have a well-known, medium-sized cellular sub-metabolome. These features facilitate both data processing and pathway mapping. In this experiment, students isolate mitochondria from potatoes, extract the metabolites, and analyze them by high-resolution mass spectrometry (using an FT-ICR mass spectrometer). The resulting mass list is submitted to an online program for metabolite identification, and compounds associated with mitochondrial pathways can be highlighted in a metabolic network map. © 2015 The International Union of Biochemistry and Molecular Biology.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age

PubMed Central

Al-Salameh, Abdallah; Croixmarie, Vincent; Masson, Perrine; Corruble, Emmanuelle; Fève, Bruno; Colle, Romain; Ripoll, Laurent; Walther, Bernard; Boursier-Neyret, Claire; Werner, Erwan; Becquemont, Laurent; Chanson, Philippe

2017-01-01

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the “normal” metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the “normal” metabolome and its main sources of variation. PMID:28278231

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

PubMed

Trabado, Séverine; Al-Salameh, Abdallah; Croixmarie, Vincent; Masson, Perrine; Corruble, Emmanuelle; Fève, Bruno; Colle, Romain; Ripoll, Laurent; Walther, Bernard; Boursier-Neyret, Claire; Werner, Erwan; Becquemont, Laurent; Chanson, Philippe

2017-01-01

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.

Bioprospecting Chemical Diversity and Bioactivity in a Marine Derived Aspergillus terreus.

PubMed

Adpressa, Donovon A; Loesgen, Sandra

2016-02-01

A comparative metabolomic study of a marine derived fungus (Aspergillus terreus) grown under various culture conditions is presented. The fungus was grown in eleven different culture conditions using solid agar, broth cultures, or grain based media (OSMAC). Multivariate analysis of LC/MS data from the organic extracts revealed drastic differences in the metabolic profiles and guided our subsequent isolation efforts. The compound 7-desmethylcitreoviridin was isolated and identified, and is fully described for the first time. In addition, 16 known fungal metabolites were also isolated and identified. All compounds were elucidated by detailed spectroscopic analysis and tested for antibacterial activities against five human pathogens and tested for cytotoxicity. This study demonstrates that LC/MS based multivariate analysis provides a simple yet powerful tool to analyze the metabolome of a single fungal strain grown under various conditions. This approach allows environmentally-induced changes in metabolite expression to be rapidly visualized, and uses these differences to guide the discovery of new bioactive molecules. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

Nuclear Magnetic Resonance-Based Metabolomics Approach to Evaluate the Prevention Effect of Camellia nitidissima Chi on Colitis-Associated Carcinogenesis

PubMed Central

Li, Ming-Hui; Du, Hong-Zhi; Kong, Gui-Ju; Liu, Li-Bao; Li, Xin-Xin; Lin, Sen-Sen; Jia, Ai-Qun; Yuan, Sheng-Tao; Sun, Li; Wang, Jun-Song

2017-01-01

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, occurring in the colon or rectum portion of large intestine. With marked antioxidant, anti-inflammation and anti-tumor activities, Camellia nitidissima Chi has been used as an effective treatment of cancer. The azoxymethane/dextran sodium sulfate (AOM/DSS) induced CRC mice model was established and the prevention effect of C. nitidissima Chi extracts on the evolving of CRC was evaluated by examination of neoplastic lesions, histopathological inspection, serum biochemistry analysis, combined with nuclear magnetic resonance (NMR)-based metabolomics and correlation network analysis. C. nitidissima Chi extracts could significantly inhibit AOM/DSS induced CRC, relieve the colonic pathology of inflammation and ameliorate the serum biochemistry, and could significantly reverse the disturbed metabolic profiling toward the normal state. Moreover, the butanol fraction showed a better efficacy than the water-soluble fraction of C. nitidissima Chi. Further development of C. nitidissima Chi extracts as a potent CRC inhibitor was warranted. PMID:28744216

Spatio-temporal distribution and natural variation of metabolites in citrus fruits.

PubMed

Wang, Shouchuang; Tu, Hong; Wan, Jian; Chen, Wei; Liu, Xianqing; Luo, Jie; Xu, Juan; Zhang, Hongyan

2016-05-15

To study the natural variation and spatio-temporal accumulation of citrus metabolites, liquid chromatography tandem mass spectrometry (LC-MS) based metabolome analysis was performed on four fruit tissues (flavedo, albedo, segment membrane and juice sacs) and different Citrus species (lemon, pummelo and grapefruit, sweet orange and mandarin). Using a non-targeted metabolomics approach, more than 2000 metabolite signals were detected, from which more than 54 metabolites, including amino acids, flavonoids and limonoids, were identified/annotated. Differential accumulation patterns of both primary metabolites and secondary metabolites in various tissues and species were revealed by our study. Further investigation indicated that flavedo accumulates more flavonoids while juice sacs contain more amino acids. Besides this, cluster analysis based on the levels of metabolites detected in 47 individual Citrus accessions clearly grouped them into four distinct clusters: pummelos and grapefruits, lemons, sweet oranges and mandarins, while the cluster of pummelos and grapefruits lay distinctly apart from the other three species. Copyright © 2015 Elsevier Ltd. All rights reserved.

A metabolomics-based approach identifies changes in the small molecular weight compound composition of the peanut as a result of dry-roasting.

PubMed

Klevorn, Claire M; Dean, Lisa L

2018-02-01

Raw peanuts in the USA are subjected to thermal processing, such as dry-roasting, prior to consumption. A multi-instrument metabolomics-based platform along with targeted analyses was used to determine changes in the low-molecular-weight compound composition of peanuts due to dry-roasting. Runner and virginia-type peanut seeds were characterized using several analytical platforms including (RP)/UPLC-MS/MS (positive and negative ion mode ESI) and HILIC/UPLC-MS/MS with negative ion mode ESI. Of the 383 compounds identified, 16 compounds were unique to the roasted peanuts. Using pathway analysis, compounds associated with arginine and proline metabolism were found to be the most changed. Products of chemical degradation and compounds contained within the vesicular bodies of the peanut increased after roasting. Dry-roasting had a significant impact on the levels and types of low-molecular-weight compounds present. These findings provide useful information about composition changes due to roasting. Published by Elsevier Ltd.

Atmospheric vs. anaerobic processing of metabolome samples for the metabolite profiling of a strict anaerobic bacterium, Clostridium acetobutylicum.

PubMed

Lee, Sang-Hyun; Kim, Sooah; Kwon, Min-A; Jung, Young Hoon; Shin, Yong-An; Kim, Kyoung Heon

2014-12-01

Well-established metabolome sample preparation is a prerequisite for reliable metabolomic data. For metabolome sampling of a Gram-positive strict anaerobe, Clostridium acetobutylicum, fast filtration and metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v) at -20°C under anaerobic conditions has been commonly used. This anaerobic metabolite processing method is laborious and time-consuming since it is conducted in an anaerobic chamber. Also, there have not been any systematic method evaluation and development of metabolome sample preparation for strict anaerobes and Gram-positive bacteria. In this study, metabolome sampling and extraction methods were rigorously evaluated and optimized for C. acetobutylicum by using gas chromatography/time-of-flight mass spectrometry-based metabolomics, in which a total of 116 metabolites were identified. When comparing the atmospheric (i.e., in air) and anaerobic (i.e., in an anaerobic chamber) processing of metabolome sample preparation, there was no significant difference in the quality and quantity of the metabolomic data. For metabolite extraction, pure methanol at -20°C was a better solvent than acetonitrile/methanol/water (2:2:1, v/v/v) at -20°C that is frequently used for C. acetobutylicum, and metabolite profiles were significantly different depending on extraction solvents. This is the first evaluation of metabolite sample preparation under aerobic processing conditions for an anaerobe. This method could be applied conveniently, efficiently, and reliably to metabolome analysis for strict anaerobes in air. © 2014 Wiley Periodicals, Inc.

An overview of methods using (13)C for improved compound identification in metabolomics and natural products.

PubMed

Clendinen, Chaevien S; Stupp, Gregory S; Ajredini, Ramadan; Lee-McMullen, Brittany; Beecher, Chris; Edison, Arthur S

2015-01-01

Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize (13)C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) (13)C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two (13)C-based approaches. For samples at natural abundance, we have developed a workflow to obtain (13)C-(13)C and (13)C-(1)H statistical correlations using 1D (13)C and (1)H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct (13)C-(13)C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which (13)C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.

Metabolomics as a tool to identify biomarkers to predict and improve outcomes in reproductive medicine: a systematic review.

PubMed

Bracewell-Milnes, Timothy; Saso, Srdjan; Abdalla, Hossam; Nikolau, Dimitrios; Norman-Taylor, Julian; Johnson, Mark; Holmes, Elaine; Thum, Meen-Yau

2017-11-01

Infertility is a complex disorder with significant medical, psychological and financial consequences for patients. With live-birth rates per cycle below 30% and a drive from the Human Fertilisation and Embryology Authority (HFEA) to encourage single embryo transfer, there is significant research in different areas aiming to improve success rates of fertility treatments. One such area is investigating the causes of infertility at a molecular level, and metabolomics techniques provide a platform for studying relevant biofluids in the reproductive tract. The aim of this systematic review is to examine the recent findings for the potential application of metabolomics to female reproduction, specifically to the metabolomics of follicular fluid (FF), embryo culture medium (ECM) and endometrial fluid. To our knowledge no other systematic review has investigated this topic. English peer-reviewed journals on PubMed, Science Direct, SciFinder, were systematically searched for studies investigating metabolomics and the female reproductive tract with no time restriction set for publications. Studies were assessed for quality using the risk of bias assessment and ROBIN-I. There were 21 studies that met the inclusion criteria and were included in the systematic review. Metabolomic studies have been employed for the compositional analysis of various biofluids in the female reproductive tract, including FF, ECM, blastocoele fluid and endometrial fluid. There is some weak evidence that metabolomics technologies studying ECM might be able to predict the viability of individual embryos and implantation rate better than standard embryo morphology, However these data were not supported by randomized the controlled trials (RCTs) which showed no evidence that using metabolomics is able to improve the most important reproductive outcomes, such as clinical pregnancy and live-birth rates. This systematic review provides guidance for future metabolomic studies on biofluids of the female reproductive tract, with a summary of the current findings, promise and pitfalls in metabolomic techniques. The approaches discussed can be adapted by other metabolomic studies. A range of sophisticated modern metabolomic techniques are now more widely available and have been applied to the analysis of the female reproductive tract. However, this review has revealed the paucity of metabolomic studies in the field of fertility and the inconsistencies of findings between different studies, as well as a lack of research examining the metabolic effects of various gynecological diseases. By incorporating metabolomic technology into an increased number of well designed studies, a much greater understanding of infertility at a molecular level could be achieved. However, there is currently no evidence for the use of metabolomics in clinical practice to improve fertility outcomes. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A Metabolome-Wide Study of Dry Eye Disease Reveals Serum Androgens as Biomarkers.

PubMed

Vehof, Jelle; Hysi, Pirro G; Hammond, Christopher J

2017-04-01

To test the association between serum metabolites and dry eye disease (DED) using a hypothesis-free metabolomics approach. Cross-sectional association study. A total of 2819 subjects from the population-representative TwinsUK cohort in the United Kingdom, with a mean age of 57 years (range, 17-82 years). We tested associations between 222 known serum metabolites and DED. All subjects underwent nontargeted metabolomic analysis of plasma samples using gas and liquid chromatography in combination with mass spectrometry (Metabolon Inc., Durham, NC). Dry eye disease was defined from the validated Short Questionnaire for Dry Eye Syndrome (SQDES) as a previous diagnosis of DED by a clinician or "often" or "constant" symptoms of dryness and irritation. Analyses were performed with linear mixed effect models that included age, BMI, and sex as covariates, corrected for multiple testing. Primary outcome was DED as defined by the SQDES, and secondary outcomes were symptom score of DED and a clinical diagnosis of DED. Prevalence of DED as defined by the SQDES was 15.5% (n = 436). A strong and metabolome-wide significant association with DED was found with decreased levels of the metabolites androsterone sulfate (P = 0.00030) and epiandrosterone sulfate (P = 0.00036). Three other metabolites involved in androgen metabolism, 4-androsten-3beta,17beta-diol disulfate 1 and 2, and dehydroepiandrosterone sulfate, were the next most strongly associated of the 222 metabolites, but did not reach metabolome-wide significance. Dryness and irritation symptoms, as opposed to a clinical diagnosis, were particularly strongly associated with decreased androgen steroid metabolites, with all reaching metabolome-wide significance (androsterone sulfate, P = 0.000000029; epiandrosterone sulfate, P = 0.0000040; 4-androsten-3beta,17beta-diol disulfate 1, P = 0.000016; 4-androsten-3beta,17beta-diol disulfate 2, P = 0.000064; and dehydroepiandrosterone sulfate, P = 0.00011). Of these 5 androgens, epiandrosterone sulfate (P = 0.0076) was most associated with 2-year incidence of clinician-diagnosed DED. In addition, we found decreased glycerophosphocholines to be associated with DED, although not at metabolome-wide significance. This hypothesis-free metabolomic approach found decreased serum androgens to be highly associated with DED and adds important evidence to the growing body of research that links androgens to ocular surface disease and DED. Copyright © 2017 American Academy of Ophthalmology. All rights reserved.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

PubMed

Zha, Haihong; Cai, Yuping; Yin, Yandong; Wang, Zhuozhong; Li, Kang; Zhu, Zheng-Jiang

2018-03-20

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS 2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

The 1H-NMR-based metabolite profile of acute alcohol consumption: A metabolomics intervention study

PubMed Central

Mason, Shayne; Mienie, Lodewyk J.; Wevers, Ron A.; Westerhuis, Johan A.

2018-01-01

Metabolomics studies of disease conditions related to chronic alcohol consumption provide compelling evidence of several perturbed metabolic pathways underlying the pathophysiology of alcoholism. The objective of the present study was to utilize proton nuclear magnetic resonance (1H-NMR) spectroscopy metabolomics to study the holistic metabolic consequences of acute alcohol consumption in humans. The experimental design was a cross-over intervention study which included a number of substances to be consumed—alcohol, a nicotinamide adenine dinucleotide (NAD) supplement, and a benzoic acid-containing flavoured water vehicle. The experimental subjects—24 healthy, moderate-drinking young men—each provided six hourly-collected urine samples for analysis. Complete data sets were obtained from 20 of the subjects and used for data generation, analysis and interpretation. The results from the NMR approach produced complex spectral data, which could be resolved sufficiently through the application of a combination of univariate and multivariate methods of statistical analysis. The metabolite profiles resulting from acute alcohol consumption indicated that alcohol-induced NAD+ depletion, and the production of an excessive amount of reducing equivalents, greatly perturbed the hepatocyte redox homeostasis, resulting in essentially three major metabolic disturbances—up-regulated lactic acid metabolism, down-regulated purine catabolism and osmoregulation. Of these, the urinary excretion of the osmolyte sorbitol proved to be novel, and suggests hepatocyte swelling due to ethanol influx following acute alcohol consumption. Time-dependent metabolomics investigations, using designed interventions, provide a way of interpreting the variation induced by the different factors of a designed experiment, thereby also giving methodological significance to this study. The outcomes of this approach have the potential to significantly advance our understanding of the serious impact of the pathophysiological perturbations which arise from the consumption of a single, large dose of alcohol—a simulation of a widespread, and mostly naive, social practice. PMID:29746531

The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics.

PubMed

Gil Solsona, R; Boix, C; Ibáñez, M; Sancho, J V

2018-03-01

The aim of this study was to use an untargeted UHPLC-HRMS-based metabolomics approach allowing discrimination between almonds based on their origin and variety. Samples were homogenised, extracted with ACN:H 2 O (80:20) containing 0.1% HCOOH and injected in a UHPLC-QTOF instrument in both positive and negative ionisation modes. Principal component analysis (PCA) was performed to ensure the absence of outliers. Partial least squares - discriminant analysis (PLS-DA) was employed to create and validate the models for country (with five different compounds) and variety (with 20 features), showing more than 95% accuracy. Additional samples were injected and the model was evaluated with blind samples, with more than 95% of samples being correctly classified using both models. MS/MS experiments were carried out to tentatively elucidate the highlighted marker compounds (pyranosides, peptides or amino acids, among others). This study has shown the potential of high-resolution mass spectrometry to perform and validate classification models, also providing information concerning the identification of the unexpected biomarkers which showed the highest discriminant power.

The great importance of normalization of LC-MS data for highly-accurate non-targeted metabolomics.

PubMed

Mizuno, Hajime; Ueda, Kazuki; Kobayashi, Yuta; Tsuyama, Naohiro; Todoroki, Kenichiro; Min, Jun Zhe; Toyo'oka, Toshimasa

2017-01-01

The non-targeted metabolomics analysis of biological samples is very important to understand biological functions and diseases. LC combined with electrospray ionization-based MS has been a powerful tool and widely used for metabolomic analyses. However, the ionization efficiency of electrospray ionization fluctuates for various unexpected reasons such as matrix effects and intraday variations of the instrument performances. To remove these fluctuations, normalization methods have been developed. Such techniques include increasing the sensitivity, separating co-eluting components and normalizing the ionization efficiencies. Normalization techniques allow simultaneously correcting of the ionization efficiencies of the detected metabolite peaks and achieving quantitative non-targeted metabolomics. In this review paper, we focused on these normalization methods for non-targeted metabolomics by LC-MS. Copyright © 2016 John Wiley & Sons, Ltd.

Model-based confirmation of alternative substrates of mitochondrial electron transport chain.

PubMed

Kleessen, Sabrina; Araújo, Wagner L; Fernie, Alisdair R; Nikoloski, Zoran

2012-03-30

Discrimination of metabolic models based on high throughput metabolomics data, reflecting various internal and external perturbations, is essential for identifying the components that contribute to the emerging behavior of metabolic processes. Here, we investigate 12 different models of the mitochondrial electron transport chain (ETC) in Arabidopsis thaliana during dark-induced senescence in order to elucidate the alternative substrates to this metabolic pathway. Our findings demonstrate that the coupling of the proposed computational approach, based on dynamic flux balance analysis, with time-resolved metabolomics data results in model-based confirmations of the hypotheses that, during dark-induced senescence in Arabidopsis, (i) under conditions where the main substrate for the ETC are not fully available, isovaleryl-CoA dehydrogenase and 2-hydroxyglutarate dehydrogenase are able to donate electrons to the ETC, (ii) phytanoyl-CoA does not act even as an indirect substrate of the electron transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase complex, and (iii) the mitochondrial γ-aminobutyric acid transporter has functional significance in maintaining mitochondrial metabolism. Our study provides a basic framework for future in silico studies of alternative pathways in mitochondrial metabolism under extended darkness whereby the role of its components can be computationally discriminated based on available molecular profile data.

Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress.

PubMed

Akhatou, Ikram; González-Domínguez, Raúl; Fernández-Recamales, Ángeles

2016-04-01

Strawberry is one of the most economically important and widely cultivated fruit crops across the world, so that there is a growing need to develop new analytical methodologies for the authentication of variety and origin, as well as the assessment of agricultural and processing practices. In this work, an untargeted metabolomic strategy based on gas chromatography mass spectrometry (GC-MS) combined with multivariate statistical techniques was used for the first time to characterize the primary metabolome of different strawberry cultivars and to study metabolite alterations in response to multiple agronomic conditions. For this purpose, we investigated three varieties of strawberries with different sensitivity to environmental stress (Camarosa, Festival and Palomar), cultivated in soilless systems using various electrical conductivities, types of coverage and substrates. Metabolomic analysis revealed significant alterations in primary metabolites between the three strawberry cultivars grown under different crop conditions, including sugars (fructose, glucose), organic acids (malic acid, citric acid) and amino acids (alanine, threonine, aspartic acid), among others. Therefore, it could be concluded that GC-MS based metabolomics is a suitable tool to differentiate strawberry cultivars and characterize metabolomic changes associated with environmental and agronomic conditions. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Metabolomics in Population-Based Research

Cancer.gov

Metabolomics is the study of small molecules of both endogenous and exogenous origin, such as metabolic substrates and their products, lipids, small peptides, vitamins and other protein cofactors generated by metabolism, which are downstream from genes.

A Chemometrics Approach for Nuclear Magnetic Resonance Data to Characterize the Partial Metabolome Banana Peels from Southern Brazil.

PubMed

Cardoso, Sara; Maraschin, Marcelo; Peruch, Luiz Augusto Martins; Rocha, Miguel; Pereira, Aline

2017-12-13

Banana peels are well recognized as a source of important bioactive compounds, such as phenolics, carotenoids, biogenic amines, among others. As such, they have recently started to be used for industrial purposes. However, its composition seems to be strongly affected by biotic or abiotic ecological factors. Thus, this study aimed to investigate banana peels chemical composition, not only to get insights on eventual metabolic changes caused by the seasons, in southern Brazil, but also to identify the most relevant metabolites for these processes. To achieve this, a Nuclear magnetic resonance (NMR)-based metabolic profiling strategy was adopted, followed by chemometrics analysis, using the specmine package for the R environment, and metabolite identification. The results showed that the metabolomic approach adopted allowed identifying a series of primary and secondary metabolites in the aqueous extracts investigated. Besides, over the seasons the metabolic profiles of the banana peels showed to contain biologically active compounds relevant to the skin wound healing process, indicating the biotechnological potential of that raw material.

Metabolomics approach of infant formula for the evaluation of contamination and degradation using hydrophilic interaction liquid chromatography coupled with mass spectrometry.

PubMed

Inoue, Koichi; Tanada, Chihiro; Sakamoto, Tasuku; Tsutsui, Haruhito; Akiba, Takashi; Min, Jun Zhe; Todoroki, Kenichiro; Yamano, Yutaka; Toyo'oka, Toshimasa

2015-08-15

In this study including the field of metabolomics approach for food, the evaluation of untargeted compounds using HILIC-ESI/TOF/MS and multivariate statistical analysis method is proposed for the assessment of classification, contamination and degradation of infant formula. HILIC mode is used to monitor more detected numbers in infant formulas in the ESI-positive scan mode than the reversed phase. The repeatability of the non-targeted contents from 4 kinds of infant formulas based on PCA was less than the relative standard deviation of 15% in all groups. The PCA pattern showed that significant differences in the classification of types and origins, the contamination of melamine and the degradations for one week were evaluated using HILIC-ESI/TOF/MS. In the S-plot from the degradation test, we could identify two markers by comparison to standards as nicotinic acid and nicotinamide. With this strategy, the differences from the untargeted compounds could be utilized for quality and safety assessment of infant formula. Copyright © 2015 Elsevier Ltd. All rights reserved.

A HPLC-Q-TOF-MS-based urinary metabolomic approach to identification of potential biomarkers of metabolic syndrome.

PubMed

Yu, Zhi-rui; Ning, Yu; Yu, Hao; Tang, Nai-jun

2014-04-01

Metabolic syndrome (MetS) is a serious threat to public health worldwide with an increased risk of developing type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. In this study, a urinary metabolomic approach was performed on high performance liquid chromatography quadrupole time-of-flight mass spectrometry to discriminate 36 male MetS patients and 36 sex and age matched healthy controls. Pattern recognition analyses (principal component analysis and orthogonal projections to latent structures discriminate analysis) commonly demonstrated the difference between MetS patients and no-MetS subjects. This study found 8 metabolites that showed significant changes in patients with MetS, including branch-chain and aromatic amino acids (leucine, tyrosine, phenylalanine and tryptophan), short-chain acylcanitine (tiglylcarnitine), tricarboxylic acid (TCA) cycle intermediate (cis-aconitic acid) and glucuronidated products (cortolone-3-glucuronide and tetrahydroaldosterone-3-glucuronide). The candidate biomarkers revealed in this study could be useful in providing clues for further research focusing on the in-depth investigation of the cause of and cure for MetS.

Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics.

PubMed

Wu, Yiman; Li, Liang

2012-12-18

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with (12)C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with (13)C-dansylation. Equal amounts of the (12)C-labeled individual sample and the (13)C-labeled pooled urine standard were mixed for LC-MS analysis. This way of concentration normalization among different samples with varying concentrations of total metabolites was found to be critical for generating reliable metabolome profiles for comparison.

Diagnosis of gastroenterological diseases by metabolome analysis using gas chromatography-mass spectrometry.

PubMed

Yoshida, Masaru; Hatano, Naoya; Nishiumi, Shin; Irino, Yasuhiro; Izumi, Yoshihiro; Takenawa, Tadaomi; Azuma, Takeshi

2012-01-01

Recently, metabolome analysis has been increasingly applied to biomarker detection and disease diagnosis in medical studies. Metabolome analysis is a strategy for studying the characteristics and interactions of low molecular weight metabolites under a specific set of conditions and is performed using mass spectrometry and nuclear magnetic resonance spectroscopy. There is a strong possibility that changes in metabolite levels reflect the functional status of a cell because alterations in their levels occur downstream of DNA, RNA, and protein. Therefore, the metabolite profile of a cell is more likely to represent the current status of a cell than DNA, RNA, or protein. Thus, owing to the rapid development of mass spectrometry analytical techniques metabolome analysis is becoming an important experimental method in life sciences including the medical field. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry, and matrix assisted laser desorption ionization-mass spectrometry. Then, the findings of studies about GC-MS-based metabolome analysis of gastroenterological diseases are summarized, and our research results are also introduced. Finally, we discuss the realization of disease diagnosis by metabolome analysis. The development of metabolome analysis using mass spectrometry will aid the discovery of novel biomarkers, hopefully leading to the early detection of various diseases.

An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin.

PubMed

Ruiz-Aracama, Ainhoa; Peijnenburg, Ad; Kleinjans, Jos; Jennen, Danyel; van Delft, Joost; Hellfrisch, Caroline; Lommen, Arjen

2011-05-20

In vitro cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied in vitro but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on in vitro systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the in vitro model system and model toxicant, respectively. The study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD. Untargeted profiling of the polar and apolar metabolites of in vitro cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.

A Trans-omics Mathematical Analysis Reveals Novel Functions of the Ornithine Metabolic Pathway in Cancer Stem Cells

NASA Astrophysics Data System (ADS)

Koseki, Jun; Matsui, Hidetoshi; Konno, Masamitsu; Nishida, Naohiro; Kawamoto, Koichi; Kano, Yoshihiro; Mori, Masaki; Doki, Yuichiro; Ishii, Hideshi

2016-02-01

Bioinformatics and computational modelling are expected to offer innovative approaches in human medical science. In the present study, we performed computational analyses and made predictions using transcriptome and metabolome datasets obtained from fluorescence-based visualisations of chemotherapy-resistant cancer stem cells (CSCs) in the human oesophagus. This approach revealed an uncharacterized role for the ornithine metabolic pathway in the survival of chemotherapy-resistant CSCs. The present study fastens this rationale for further characterisation that may lead to the discovery of innovative drugs against robust CSCs.

Early Prediction of Lupus Nephritis Using Advanced Proteomics

DTIC Science & Technology

2011-06-01

spectroscopy-based metabolomic profiling , and apolipoprotein D, lipocalin-like prostaglandin D synthetase, hemopexin, ceruloplasmin, -1-B glycoprotein and...will be confirmed and enhanced using NMR- and MS-based metabonomics , by Dr. Michael Kennedy, Miami University. Changes in proteomic profiles will be...based metabolomic profiling , and apolipoprotein D, lipocalin-like prostaglandin D synthetase, hemopexin, ceruloplasmin, -1-B glycoprotein and

Comparison of subacute effects of two types of pyrethroid insecticides using metabolomics methods.

PubMed

Miao, Jiyan; Wang, Dezhen; Yan, Jin; Wang, Yao; Teng, Miaomiao; Zhou, Zhiqiang; Zhu, Wentao

2017-11-01

In this study, 1 H NMR based metabolomics analysis, LC-MS/MS based serum metabolomics and histopathology techniques were used to investigate the toxic effects of subacute exposure to two types of pyrethroid insecticides bifenthrin and lambda-cyhalothrin in mice. Metabolomic analysis of tissues extracts and serum showed that these two types of pyrethroid insecticides resulted in alterations of metabolites in the liver, kidney and serum of mice. Based on the altered metabolites, several significant pathways were identified, which are associated with gut microbial metabolism, lipid metabolism, nucleotide catabolism, tyrosine metabolism and energy metabolism. The results showed that bifenthrin and lambda-cyhalothrin have similarities in disruption of metabolic pathways in kidney, indicating that the toxicological mechanisms of these two types of insecticides have some likeness to each other. This study may provide novel insight into revealing differences of toxicological mechanisms between these two types of pyrethroid insecticides. Copyright © 2017 Elsevier Inc. All rights reserved.

Conventional and Accelerated-Solvent Extractions of Green Tea (Camellia sinensis) for Metabolomics-based Chemometrics

PubMed Central

Kellogg, Joshua J.; Wallace, Emily D.; Graf, Tyler N.; Oberlies, Nicholas H.; Cech, Nadja B.

2018-01-01

Metabolomics has emerged as an important analytical technique for multiple applications. The value of information obtained from metabolomics analysis depends on the degree to which the entire metabolome is present and the reliability of sample treatment to ensure reproducibility across the study. The purpose of this study was to compare methods of preparing complex botanical extract samples prior to metabolomics profiling. Two extraction methodologies, accelerated solvent extraction and a conventional solvent maceration, were compared using commercial green tea [Camellia sinensis (L.) Kuntze (Theaceae)] products as a test case. The accelerated solvent protocol was first evaluated to ascertain critical factors influencing extraction using a D-optimal experimental design study. The accelerated solvent and conventional extraction methods yielded similar metabolite profiles for the green tea samples studied. The accelerated solvent extraction yielded higher total amounts of extracted catechins, was more reproducible, and required less active bench time to prepare the samples. This study demonstrates the effectiveness of accelerated solvent as an efficient methodology for metabolomics studies. PMID:28787673

The Use of Functional Genomics in Conjunction with Metabolomics for Mycobacterium tuberculosis Research

PubMed Central

Swanepoel, Conrad C.

2014-01-01

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a fatal infectious disease, resulting in 1.4 million deaths globally per annum. Over the past three decades, genomic studies have been conducted in an attempt to elucidate the functionality of the genome of the pathogen. However, many aspects of this complex genome remain largely unexplored, as approaches like genomics, proteomics, and transcriptomics have failed to characterize them successfully. In turn, metabolomics, which is relatively new to the “omics” revolution, has shown great potential for investigating biological systems or their modifications. Furthermore, when these data are interpreted in combination with previously acquired genomics, proteomics and transcriptomics data, using what is termed a systems biology approach, a more holistic understanding of these systems can be achieved. In this review we discuss how metabolomics has contributed so far to characterizing TB, with emphasis on the resulting improved elucidation of M. tuberculosis in terms of (1) metabolism, (2) growth and replication, (3) pathogenicity, and (4) drug resistance, from the perspective of systems biology. PMID:24771957

A Multiplatform Metabolomic Approach to the Basis of Antimonial Action and Resistance in Leishmania infantum

PubMed Central

Castilho-Martins, Emerson A.; Tavares, Marina F. M.; Barbas, Coral; López-Gonzálvez, Ángeles; Rivas, Luis

2015-01-01

There is a rising resistance against antimony drugs, the gold-standard for treatment until some years ago. That is a serious problem due to the paucity of drugs in current clinical use. In a research to reveal how these drugs affect the parasite during treatment and to unravel the underlying basis for their resistance, we have employed metabolomics to study treatment in Leishmania infantum promastigotes. This was accomplished first through the untargeted analysis of metabolic snapshots of treated and untreated parasites both resistant and responders, utilizing a multiplatform approach to give the widest as possible coverage of the metabolome, and additionally through novel monitoring of the origin of the detected alterations through a 13C traceability experiment. Our data stress a multi-target metabolic alteration with treatment, affecting in particular the cell redox system that is essential to cope with detoxification and biosynthetic processes. Additionally, relevant changes were noted in amino acid metabolism. Our results are in agreement with other authors studying other Leishmania species. PMID:26161866

Tailored liquid chromatography-mass spectrometry analysis improves the coverage of the intracellular metabolome of HepaRG cells.

PubMed

Cuykx, Matthias; Negreira, Noelia; Beirnaert, Charlie; Van den Eede, Nele; Rodrigues, Robim; Vanhaecke, Tamara; Laukens, Kris; Covaci, Adrian

2017-03-03

Metabolomics protocols are often combined with Liquid Chromatography-Mass Spectrometry (LC-MS) using mostly reversed phase chromatography coupled to accurate mass spectrometry, e.g. quadrupole time-of-flight (QTOF) mass spectrometers to measure as many metabolites as possible. In this study, we optimised the LC-MS separation of cell extracts after fractionation in polar and non-polar fractions. Both phases were analysed separately in a tailored approach in four different runs (two for the non-polar and two for the polar-fraction), each of them specifically adapted to improve the separation of the metabolites present in the extract. This approach improves the coverage of a broad range of the metabolome of the HepaRG cells and the separation of intra-class metabolites. The non-polar fraction was analysed using a C18-column with end-capping, mobile phase compositions were specifically adapted for each ionisation mode using different co-solvents and buffers. The polar extracts were analysed with a mixed mode Hydrophilic Interaction Liquid Chromatography (HILIC) system. Acidic metabolites from glycolysis and the Krebs cycle, together with phosphorylated compounds, were best detected with a method using ion pairing (IP) with tributylamine and separation on a phenyl-hexyl column. Accurate mass detection was performed with the QTOF in MS-mode only using an extended dynamic range to improve the quality of the dataset. Parameters with the greatest impact on the detection were the balance between mass accuracy and linear range, the fragmentor voltage, the capillary voltage, the nozzle voltage, and the nebuliser pressure. By using a tailored approach for the intracellular HepaRG metabolome, consisting of three different LC techniques, over 2200 metabolites can be measured with a high precision and acceptable linear range. The developed method is suited for qualitative untargeted LC-MS metabolomics studies. Copyright © 2017 Elsevier B.V. All rights reserved.

UHPLC-Q-TOF-MS-based metabolomics approach to compare the saponin compositions of Xueshuantong injection and Xuesaitong injection.

PubMed

Yao, Changliang; Yang, Wenzhi; Zhang, Jingxian; Qiu, Shi; Chen, Ming; Shi, Xiaojian; Pan, Huiqin; Wu, Wanying; Guo, Dean

2017-02-01

Various traditional Chinese medicine preparations developed from Notoginseng total saponins, including Xueshuantong injection and Xuesaitong injection, are extensively used in China to treat cardiocerebrovascular diseases. However, the difference of their saponin compositions remains unknown. An ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry based metabolomics approach was developed to probe the saponin discrimination between Xueshuantong and Xuesaitong and the related factors by large sample analysis. A highly efficient chromatographic separation was achieved on an HSS T3 column within 20 min with the holistic metabolites information recorded in the negative MS E mode. A six-step data pretreatment procedure mainly based on Progenesis QI and mass defect filtering was established. Pattern recognition chemometrics was used to discover the potential saponin markers. The saponin composition of Wuzhou Xueshuantong showed distinct discrimination from the other products. Wuzhou Xueshuantong contains more abundant protopanaxatriol-type noto-R 1 , Rg 1 , Re, and protopanaxadiol-type Rb 1 , but less Rd and other low-polarity protopanaxadiol-type ginsenosides. These differences could not directly correlate to the use of different parts of Panax notoginseng, but possibly to the different preparation techniques employed by different manufacturers. These results are beneficial to the establishment of pharmacopoeia standards and the assessment of the efficacy and adverse drug reactions for these homologous products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

AQuA: An Automated Quantification Algorithm for High-Throughput NMR-Based Metabolomics and Its Application in Human Plasma.

PubMed

Röhnisch, Hanna E; Eriksson, Jan; Müllner, Elisabeth; Agback, Peter; Sandström, Corine; Moazzami, Ali A

2018-02-06

A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex 1 H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r 2 ) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.

Untargeted Metabolomics Approach in Halophiles: Understanding the Biodeterioration Process of Building Materials

PubMed Central

Adamiak, Justyna; Bonifay, Vincent; Otlewska, Anna; Sunner, Jan A.; Beech, Iwona B.; Stryszewska, Teresa; Kańka, Stanisław; Oracz, Joanna; Żyżelewicz, Dorota; Gutarowska, Beata

2017-01-01

The aim of the study was to explore the halophile metabolome in building materials using untargeted metabolomics which allows for broad metabolome coverage. For this reason, we used high-performance liquid chromatography interfaced to high-resolution mass spectrometry (HPLC/HRMS). As an alternative to standard microscopy techniques, we introduced pioneering Coherent Anti-stokes Raman Scattering Microscopy (CARS) to non-invasively visualize microbial cells. Brick samples saturated with salt solution (KCl, NaCl (two salinity levels), MgSO4, Mg(NO3)2), were inoculated with the mixture of preselected halophilic microorganisms, i.e., bacteria: Halobacillus styriensis, Halobacillus naozhouensis, Halobacillus hunanensis, Staphylococcus succinus, Marinococcus halophilus, Virgibacillus halodenitryficans, and yeast: Sterigmatomyces halophilus and stored at 28°C and 80% relative humidity for a year. Metabolites were extracted directly from the brick samples and measured via HPLC/HRMS in both positive and negative ion modes. Overall, untargeted metabolomics allowed for discovering the interactions of halophilic microorganisms with buildings materials which together with CARS microscopy enabled us to elucidate the biodeterioration process caused by halophiles. We observed that halophile metabolome was differently affected by different salt solutions. Furthermore, we found indications for haloadaptive strategies and degradation of brick samples due to microbial pigment production as a salt stress response. Finally, we detected changes in lipid content related to changes in the structure of phospholipid bilayers and membrane fluidity. PMID:29321766

Metabolomics Guides Rational Development of a Simplified Cell Culture Medium for Drug Screening against Trypanosoma brucei

PubMed Central

Creek, Darren J.; Nijagal, Brunda; Kim, Dong-Hyun; Rojas, Federico; Matthews, Keith R.

2013-01-01

In vitro culture methods underpin many experimental approaches to biology and drug discovery. The modification of established cell culture methods to make them more biologically relevant or to optimize growth is traditionally a laborious task. Emerging metabolomic technology enables the rapid evaluation of intra- and extracellular metabolites and can be applied to the rational development of cell culture media. In this study, untargeted semiquantitative and targeted quantitative metabolomic analyses of fresh and spent media revealed the major nutritional requirements for the growth of bloodstream form Trypanosoma brucei. The standard culture medium (HMI11) contained unnecessarily high concentrations of 32 nutrients that were subsequently removed to make the concentrations more closely resemble those normally found in blood. Our new medium, Creek's minimal medium (CMM), supports in vitro growth equivalent to that in HMI11 and causes no significant perturbation of metabolite levels for 94% of the detected metabolome (<3-fold change; α = 0.05). Importantly, improved sensitivity was observed for drug activity studies in whole-cell phenotypic screenings and in the metabolomic mode of action assays. Four-hundred-fold 50% inhibitory concentration decreases were observed for pentamidine and methotrexate, suggesting inhibition of activity by nutrients present in HMI11. CMM is suitable for routine cell culture and offers important advantages for metabolomic studies and drug activity screening. PMID:23571546

Recent Highlights of Metabolomics in Chinese Medicine Syndrome Research

PubMed Central

Zhang, Ai-hua; Sun, Hui; Qiu, Shi; Wang, Xi-jun

2013-01-01

Chinese medicine syndrome (CMS, “ZHENG” in Chinese) is an understanding of the regularity of disease occurrence and development as well as a certain stage of a comprehensive response of patients with body condition. However, because of the complexity of CMS and the limitation of present investigation method, the research for deciphering the scientific basis and systematic features of CMS is difficult to go further. Metabolomics enables mapping of early biochemical changes in disease and hence provides an opportunity to develop predictive biomarkers. Moreover, its method and design resemble those of traditional Chinese medicine (TCM) which focuses on human disease via the integrity of close relationship between body and syndromes. In the systemic context, metabolomics has a convergence with TCM syndrome; therefore it could provide useful tools for exploring essence of CMS disease, facilitating personalized TCM, and will help to in-depth understand CMS. The integration of the metabolomics and CMS aspects will give promise to bridge the gap between Chinese and Western medicine and help catch the traditional features of CMS. In this paper, particular attention will be paid to the past successes in applications of robust metabolomic approaches to contribute to low-molecular-weight metabolites (biomarkers) discovery in CMS research and development. PMID:24302964

A network pharmacology-integrated metabolomics strategy for clarifying the difference between effective compounds of raw and processed Farfarae flos by ultra high-performance liquid chromatography-quadrupole-time of flight mass spectrometry.

PubMed

Ding, Mingya; Li, Zhen; Yu, Xie-An; Zhang, Dong; Li, Jin; Wang, Hui; He, Jun; Gao, Xiu-Mei; Chang, Yan-Xu

2018-07-15

This study aimed to clarify the difference between the effective compounds of raw and processed Farfarae flos using a network pharmacology-integrated metabolomics strategy. First, metabolomics data were obtained by ultra high-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS). Then, metabolomics analysis was developed to screen for the influential compounds that were different between raw and processed Farfarae flos. Finally, a network pharmacology approach was applied to verify the activity of the screened compounds. As a result, 4 compounds (chlorogenic acid, caffeic acid, rutin and isoquercitrin) were successfully screened, identified, quantified and verified as the most influential effective compounds. They may synergistically inhibit the p38, JNK and ERK-mediated pathways, which would induce the inhibition of the expression of the IFA virus. The results revealed that the proposed network pharmacology-integrated metabolomics strategy was a powerful tool for discovering the effective compounds that were responsible for the difference between raw and processed Chinese herbs. Copyright © 2018 Elsevier B.V. All rights reserved.

Meta-analysis of global metabolomics and proteomics data to link alterations with phenotype

DOE PAGES

Patti, Gary J.; Tautenhahn, Ralf; Fonslow, Bryan R.; ...

2011-01-01

Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiplemore » pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis.« less

The effects of age and dietary restriction on the tissue-specific metabolome of Drosophila

PubMed Central

Laye, Matthew J; Tran, ViLinh; Jones, Dean P; Kapahi, Pankaj; Promislow, Daniel E L

2015-01-01

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age-related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease-associated phenotypes. Here, we use high-resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient-rich ad libitum (AL) or nutrient-restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age-related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age. PMID:26085309

Influence of the collection tube on metabolomic changes in serum and plasma.

PubMed

López-Bascón, M A; Priego-Capote, F; Peralbo-Molina, A; Calderón-Santiago, M; Luque de Castro, M D

2016-04-01

Major threats in metabolomics clinical research are biases in sampling and preparation of biological samples. Bias in sample collection is a frequently forgotten aspect responsible for uncontrolled errors in metabolomics analysis. There is a great diversity of blood collection tubes for sampling serum or plasma, which are widely used in metabolomics analysis. Most of the existing studies dealing with the influence of blood collection on metabolomics analysis have been restricted to comparison between plasma and serum. However, polymeric gel tubes, which are frequently proposed to accelerate the separation of serum and plasma, have not been studied. In the present research, samples of serum or plasma collected in polymeric gel tubes were compared with those taken in conventional tubes from a metabolomics perspective using an untargeted GC-TOF/MS approach. The main differences between serum and plasma collected in conventional tubes affected to critical pathways such as the citric acid cycle, metabolism of amino acids, fructose and mannose metabolism and that of glycerolipids, and pentose and glucuronate interconversion. On the other hand, the polymeric gel only promoted differences at the metabolite level in serum since no critical differences were observed between plasma collected with EDTA tubes and polymeric gel tubes. Thus, the main changes were attributable to serum collected in gel and affected to the metabolism of amino acids such as alanine, proline and threonine, the glycerolipids metabolism, and two primary metabolites such as aconitic acid and lactic acid. Therefore, these metabolite changes should be taken into account in planning an experimental protocol for metabolomics analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative metabolomics of the thermophilic methylotroph Bacillus methanolicus.

PubMed

Carnicer, Marc; Vieira, Gilles; Brautaset, Trygve; Portais, Jean-Charles; Heux, Stephanie

2016-06-01

The gram-positive bacterium Bacillus methanolicus MGA3 is a promising candidate for methanol-based biotechnologies. Accurate determination of intracellular metabolites is crucial for engineering this bacteria into an efficient microbial cell factory. Due to the diversity of chemical and cell properties, an experimental protocol validated on B. methanolicus is needed. Here a systematic evaluation of different techniques for establishing a reliable basis for metabolome investigations is presented. Metabolome analysis was focused on metabolites closely linked with B. methanolicus central methanol metabolism. As an alternative to cold solvent based procedures, a solvent-free quenching strategy using stainless steel beads cooled to -20 °C was assessed. The precision, the consistency of the measurements, and the extent of metabolite leakage from quenched cells were evaluated in procedures with and without cell separation. The most accurate and reliable performance was provided by the method without cell separation, as significant metabolite leakage occurred in the procedures based on fast filtration. As a biological test case, the best protocol was used to assess the metabolome of B. methanolicus grown in chemostat on methanol at two different growth rates and its validity was demonstrated. The presented protocol is a first and helpful step towards developing reliable metabolomics data for thermophilic methylotroph B. methanolicus. This will definitely help for designing an efficient methylotrophic cell factory.

Metabolomics by Gas Chromatography-Mass Spectrometry: the combination of targeted and untargeted profiling

PubMed Central

Fiehn, Oliver

2016-01-01

Gas chromatography-mass spectrometry (GC-MS)-based metabolomics is ideal for identifying and quantitating small molecular metabolites (<650 daltons), including small acids, alcohols, hydroxyl acids, amino acids, sugars, fatty acids, sterols, catecholamines, drugs, and toxins, often using chemical derivatization to make these compounds volatile enough for gas chromatography. This unit shows that on GC-MS- based metabolomics easily allows integrating targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. Complemented by database annotations using large spectral libraries and validated, standardized standard operating procedures, GC-MS can identify and semi-quantify over 200 compounds per study in human body fluids (e.g., plasma, urine or stool) samples. Deconvolution software enables detection of more than 300 additional unidentified signals that can be annotated through accurate mass instruments with appropriate data processing workflows, similar to liquid chromatography-MS untargeted profiling (LC-MS). Hence, GC-MS is a mature technology that not only uses classic detectors (‘quadrupole’) but also target mass spectrometers (‘triple quadrupole’) and accurate mass instruments (‘quadrupole-time of flight’). This unit covers the following aspects of GC-MS-based metabolomics: (i) sample preparation from mammalian samples, (ii) acquisition of data, (iii) quality control, and (iv) data processing. PMID:27038389

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

The WEIZMASS spectral library for high-confidence metabolite identification

NASA Astrophysics Data System (ADS)

Shahaf, Nir; Rogachev, Ilana; Heinig, Uwe; Meir, Sagit; Malitsky, Sergey; Battat, Maor; Wyner, Hilary; Zheng, Shuning; Wehrens, Ron; Aharoni, Asaph

2016-08-01

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Fatty acids and small organic compounds bind to mineralo-organic nanoparticles derived from human body fluids as revealed by metabolomic analysis.

PubMed

Martel, Jan; Wu, Cheng-Yeu; Hung, Cheng-Yu; Wong, Tsui-Yin; Cheng, Ann-Joy; Cheng, Mei-Ling; Shiao, Ming-Shi; Young, John D

2016-03-14

Nanoparticles entering the human body instantly become coated with a "protein corona" that influences the effects and distribution of the particles in vivo. Yet, whether nanoparticles may bind to other organic compounds remains unclear. Here we use an untargeted metabolomic approach based on ultra-performance liquid chromatography and quadruple time-of-flight mass spectrometry to identify the organic compounds that bind to mineral nanoparticles formed in human body fluids (serum, plasma, saliva, and urine). A wide range of organic compounds is identified, including fatty acids, glycerophospholipids, amino acids, sugars, and amides. Our results reveal that, in addition to the proteins identified previously, nanoparticles harbor an "organic corona" containing several fatty acids which may affect particle-cell interactions in vivo. This study provides a platform to study the organic corona of biological and synthetic nanoparticles found in the human body.

Metabolomics Reveals that Dietary Ferulic Acid and Quercetin Modulate Metabolic Homeostasis in Rats.

PubMed

Zhang, Limin; Dong, Manyuan; Guangyong Xu; Yuan Tian; Tang, Huiru; Wang, Yulan

2018-02-21

Phenolic compounds ingestion has been shown to have potential preventive and therapeutic effects against various metabolic diseases such as obesity and cancer. To provide a better understanding of these potential benefit effects, we investigated the metabolic alterations in urine and feces of rat ingested ferulic acid (FA) and quercetin (Qu) using NMR-based metabolomics approach. Our results suggested that dietary FA and/or Qu significantly decreased short chain fatty acids and elevated oligosaccharides in the feces, implying that dietary FA and Qu may modulate gut microbial community with inhibition of bacterial fermentation of dietary fibers. We also found that dietary FA and/or Qu regulated several host metabolic pathways including TCA cycle and energy metabolism, bile acid, amino acid, and nucleic acid metabolism. These biological effects suggest that FA and Qu display outstanding bioavailability and bioactivity and could be used for treatment of some metabolic syndromes, such as inflammatory bowel diseases and obesity.

Comparative metabolomics of drought acclimation in model and forage legumes.

PubMed

Sanchez, Diego H; Schwabe, Franziska; Erban, Alexander; Udvardi, Michael K; Kopka, Joachim

2012-01-01

Water limitation has become a major concern for agriculture. Such constraints reinforce the urgent need to understand mechanisms by which plants cope with water deprivation. We used a non-targeted metabolomic approach to explore plastic systems responses to non-lethal drought in model and forage legume species of the Lotus genus. In the model legume Lotus. japonicus, increased water stress caused gradual increases of most of the soluble small molecules profiled, reflecting a global and progressive reprogramming of metabolic pathways. The comparative metabolomic approach between Lotus species revealed conserved and unique metabolic responses to drought stress. Importantly, only few drought-responsive metabolites were conserved among all species. Thus we highlight a potential impediment to translational approaches that aim to engineer traits linked to the accumulation of compatible solutes. Finally, a broad comparison of the metabolic changes elicited by drought and salt acclimation revealed partial conservation of these metabolic stress responses within each of the Lotus species, but only few salt- and drought-responsive metabolites were shared between all. The implications of these results are discussed with regard to the current insights into legume water stress physiology. © 2011 Blackwell Publishing Ltd.

Salivary biomarker development using genomic, proteomic and metabolomic approaches

PubMed Central

2012-01-01

The use of saliva as a diagnostic sample provides a non-invasive, cost-efficient method of sample collection for disease screening without the need for highly trained professionals. Saliva collection is far more practical and safe compared with invasive methods of sample collection, because of the infection risk from contaminated needles during, for example, blood sampling. Furthermore, the use of saliva could increase the availability of accurate diagnostics for remote and impoverished regions. However, the development of salivary diagnostics has required technical innovation to allow stabilization and detection of analytes in the complex molecular mixture that is saliva. The recent development of cost-effective room temperature analyte stabilization methods, nucleic acid pre-amplification techniques and direct saliva transcriptomic analysis have allowed accurate detection and quantification of transcripts found in saliva. Novel protein stabilization methods have also facilitated improved proteomic analyses. Although candidate biomarkers have been discovered using epigenetic, transcriptomic, proteomic and metabolomic approaches, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity, and progress towards clinical implementation. Here, we review recent developments in salivary diagnostics that have been accomplished using genomic, transcriptomic, proteomic and metabolomic approaches. PMID:23114182

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

DOE PAGES

Benton, H. Paul; Ivanisevic, Julijana; Mahieu, Nathaniel G.; ...

2014-12-12

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometrymore » data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.« less

Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS.

PubMed

Souard, Florence; Delporte, Cédric; Stoffelen, Piet; Thévenot, Etienne A; Noret, Nausicaa; Dauvergne, Bastien; Kauffmann, Jean-Michel; Van Antwerpen, Pierre; Stévigny, Caroline

2018-04-15

Coffee bean extracts are consumed all over the world as beverage and there is a growing interest in coffee leaf extracts as food supplements. The wild diversity in Coffea (Rubiaceae) genus is large and could offer new opportunities and challenges. In the present work, a metabolomics approach was implemented to examine leaf chemical composition of 9 Coffea species grown in the same environmental conditions. Leaves were analyzed by LC-HRMS and a comprehensive statistical workflow was designed. It served for univariate hypothesis testing and multivariate modeling by PCA and partial PLS-DA on the Workflow4Metabolomics infrastructure. The first two axes of PCA and PLS-DA describes more than 40% of variances with good values of explained variances. This strategy permitted to investigate the metabolomics data and their relation with botanic and genetic informations. Finally, the identification of several key metabolites for the discrimination between species was further characterized. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparative metabolomics of aging in a long-lived bat: Insights into the physiology of extreme longevity

PubMed Central

levari-Shariati, Shiva; Cooper, Lisa Noelle; Aliani, Michel

2018-01-01

Vespertilionid bats (Mammalia: Order Chiroptera) live 3–10 times longer than other mammals of an equivalent body size. At present, nothing is known of how bat fecal metabolic profiles shift with age in any taxa. This study established the feasibility of using a non-invasive, fecal metabolomics approach to examine age-related differences in the fecal metabolome of young and elderly adult big brown bats (Eptesicus fuscus) as an initial investigation into using metabolomics for age determination. Samples were collected from captive, known-aged big brown bats (Eptesicus fuscus) from 1 to over 14 years of age: these two ages represent age groups separated by approximately 75% of the known natural lifespan of this taxon. Results showed 41 metabolites differentiated young (n = 22) and elderly (n = 6) Eptesicus. Significant differences in metabolites between young and elderly bats were associated with tryptophan metabolism and incomplete protein digestion. Results support further exploration of the physiological mechanisms bats employ to achieve exceptional longevity. PMID:29715267

Serum metabolomics of Indian women with polycystic ovary syndrome using 1H NMR coupled with a pattern recognition approach.

PubMed

RoyChoudhury, Sourav; Mishra, Biswa Prasanna; Khan, Tila; Chattopadhayay, Ratna; Lodh, Indrani; Datta Ray, Chaitali; Bose, Gunja; Sarkar, Himadri S; Srivastava, Sudha; Joshi, Mamata V; Chakravarty, Baidyanath; Chaudhury, Koel

2016-10-18

Polycystic ovary syndrome (PCOS) is one of the most commonly occurring metabolic and endocrinological disorders affecting women of reproductive age. Metabolomics is an emerging field that holds promise in understanding disease pathophysiology. Recently, a few metabolomics based studies have been attempted in PCOS patients; however, none of them have included patients from the Indian population. The main objective of this study was to investigate the serum metabolomic profile of Indian women with PCOS and compare them with controls. Proton nuclear magnetic resonance ( 1 H NMR) was used to first identify the differentially expressed metabolites among women with PCOS from the Eastern region of India during the discovery phase and further validated in a separate cohort of PCOS and control subjects. Multivariate analysis of the binned spectra indicated 16 dysregulated bins in the sera of these women with PCOS. Out of these 16 bins, 13 identified bins corresponded to 12 metabolites including 8 amino acids and 4 energy metabolites. Amongst the amino acids, alanine, valine, leucine and threonine and amongst the energy metabolites, lactate and acetate were observed to be significantly up-regulated in women with PCOS when compared with controls. The remaining 4 amino acids, l-glutamine, proline, glutamate and histidine were down-regulated along with 2 energy metabolites: glucose and 3-hydroxybutyric acid. Our findings showed dysregulations in the expression of different metabolites in the serum of women with PCOS suggesting the involvement of multiple pathways including amino acid metabolism, carbohydrate/lipid metabolism, purine and pyrimidine metabolism and protein synthesis.

Metabolomic analysis can detect the composition of pasta enriched with fibre after cooking.

PubMed

Beleggia, Romina; Menga, Valeria; Platani, Cristiano; Nigro, Franca; Fragasso, Mariagiovanna; Fares, Clara

2016-07-01

Several studies have demonstrated that metabolomics has a definite place in food quality, nutritional value, and safety issues. The aim of the present study was to determine and compare the metabolites in different pasta samples with fibre, and to investigate the modifications induced in these different kinds of pasta during cooking, using a gas chromatography-mass spectrometry-based metabolomics approach. Differences were seen for some of the amino acids, which were absent in control pasta, while were present both in the commercially available high-fibre pasta (samples A-C) and the enriched pasta (samples D-F). The highest content in reducing sugars was observed in enriched samples in comparison with high-fibre pasta. The presence of stigmasterol in samples enriched with wheat bran was relevant. Cooking decreased all of the metabolites: the high-fibre pasta (A-C) and Control showed losses of amino acids and tocopherols, while for sugars and organic acids, the decrease depended on the pasta sample. The enriched pasta samples (D-F) showed the same decreases with the exception of phytosterols, and in pasta with barley the decrease of saturated fatty acids was not significant as for tocopherols in pasta with oat. Principal component analysis of the metabolites and the pasta discrimination was effective in differentiating the enriched pasta from the commercial pasta, both uncooked and cooked. The study has established that such metabolomic analyses provide useful tools in the evaluation of the changes in nutritional compounds in high-fibre and enriched pasta, both before and after cooking. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

The metabolomic approach identifies a biological signature of low-dose chronic exposure to cesium 137.

PubMed

Grison, Stéphane; Martin, Jean-Charles; Grandcolas, Line; Banzet, Nathalie; Blanchardon, Eric; Tourlonias, Elie; Defoort, Catherine; Favé, Gaëlle; Bott, Romain; Dublineau, Isabelle; Gourmelon, Patrick; Souidi, Maâmar

2012-01-01

Reports have described apparent biological effects of (137)Cs (the most persistent dispersed radionuclide) irradiation in people living in Chernobyl-contaminated territory. The sensitive analytical technology described here should now help assess the relation of this contamination to the observed effects. A rat model chronically exposed to (137)Cs through drinking water was developed to identify biomarkers of radiation-induced metabolic disorders, and the biological impact was evaluated by a metabolomic approach that allowed us to detect several hundred metabolites in biofluids and assess their association with disease states. After collection of plasma and urine from contaminated and non-contaminated rats at the end of the 9-months contamination period, analysis with a LC-MS system detected 742 features in urine and 1309 in plasma. Biostatistical discriminant analysis extracted a subset of 26 metabolite signals (2 urinary, 4 plasma non-polar, and 19 plasma polar metabolites) that in combination were able to predict from 68 up to 94% of the contaminated rats, depending on the prediction method used, with a misclassification rate as low as 5.3%. The difference in this metabolic score between the contaminated and non-contaminated rats was highly significant (P = 0.019 after ANOVA cross-validation). In conclusion, our proof-of-principle study demonstrated for the first time the usefulness of a metabolomic approach for addressing biological effects of chronic low-dose contamination. We can conclude that a metabolomic signature discriminated (137)Cs-contaminated from control animals in our model. Further validation is nevertheless required together with full annotation of the metabolic indicators.

A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake.

PubMed

Gibbons, Helena; McNulty, Breige A; Nugent, Anne P; Walton, Janette; Flynn, Albert; Gibney, Michael J; Brennan, Lorraine

2015-03-01

The association between sugar-sweetened beverages (SSBs) and health risks remains controversial. To clarify proposed links, reliable and accurate dietary assessment methods of food intakes are essential. The aim of this present work was to use a metabolomics approach to identify a panel of urinary biomarkers indicative of SSB consumption from a national food consumption survey and subsequently validate this panel in an acute intervention study. Heat map analysis was performed to identify correlations between ¹H nuclear magnetic resonance (NMR) spectral regions and SSB intakes in participants of the National Adult Nutrition Survey (n = 565). Metabolites were identified and receiver operating characteristic (ROC) analysis was performed to assess sensitivity and specificity of biomarkers. The panel of biomarkers was validated in an acute study (n = 10). A fasting first-void urine sample and postprandial samples (2, 4, 6 h) were collected after SSB consumption. After NMR spectroscopic profiling of the urine samples, multivariate data analysis was applied. A panel of 4 biomarkers-formate, citrulline, taurine, and isocitrate-were identified as markers of SSB intake. This panel of biomarkers had an area under the curve of 0.8 for ROC analysis and a sensitivity and specificity of 0.7 and 0.8, respectively. All 4 biomarkers were identified in the SSB sample. After acute consumption of an SSB drink, all 4 metabolites increased in the urine. The present metabolomics-based strategy proved to be successful in the identification of SSB biomarkers. Future work will ascertain how to translate this panel of markers for use in nutrition epidemiology. © 2015 American Society for Nutrition.

A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops.

PubMed

Martínez Bueno, María Jesús; Díaz-Galiano, Francisco José; Rajski, Łukasz; Cutillas, Víctor; Fernández-Alba, Amadeo R

2018-04-20

In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as "organic". Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors. In the present study, advanced technologies based on high performance liquid chromatography-high-resolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions. The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem). In addition, stable nitrogen isotope analysis (δ 15 N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations. Pesticide residue analyses were also applied as a well-established way to evaluate the organic production. Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and δ 15 N data provided a robust classification model in accordance with the agricultural practices. Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone). Copyright © 2018 Elsevier B.V. All rights reserved.

[Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients].

PubMed

Chen, X; Wang, K; Chen, W; Jiang, H; Deng, P C; Li, Z J; Peng, J; Zhou, Z Y; Yang, H; Huang, G X; Zeng, J

2016-07-01

By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging. A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test. Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036). The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.

Glucose enhances tilapia against Edwardsiella tarda infection through metabolome reprogramming.

PubMed

Zeng, Zao-hai; Du, Chao-Chao; Liu, Shi-Rao; Li, Hui; Peng, Xuan-Xian; Peng, Bo

2017-02-01

We have recently reported that the survival of tilapia, Oreochromis niloticus, during Edwardsiella tarda infection is tightly associated with their metabolome, where the survived O. niloticus has distinct metabolomic profile to dying O. niloticus. Glucose is the key metabolite to distinguish the survival- and dying-metabolome. More importantly, exogenous administration of glucose to the fish greatly enhances their survival for the infection, indicating the functional roles of glucose in metabolome repurposing, known as reprogramming metabolomics. However, the underlying information for the reprogramming is not yet available. Here, GC/MS based metabolomics is used to understand the mechanisms by which how exogenous glucose elevates O. niloticus, anti-infectious ability to E. tarda. Results showed that exogenous glucose promotes stearic acid and palmitic acid biosynthesis but attenuates TCA cycle to potentiate O. niloticus against bacterial infection, which is confirmed by the fact that exogenous stearic acid increases immune protection in O. niloticus against E. tarda infection in a manner of Mx protein. These results indicate that exogenous glucose reprograms O. niloticus anti-infective metabolome that characterizes elevation of stearic acid and palmitic acid and attenuation of the TCA cycle. Therefore, our results proposed a novel mechanism that glucose promotes unsaturated fatty acid biosynthesis to cope with infection, thereby highlighting a potential way of enhancing fish immunity in aquaculture. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bisphenol A Alters n-6 Fatty Acid Composition and Decreases Antioxidant Enzyme Levels in Rat Testes: A LC-QTOF-Based Metabolomics Study

PubMed Central

Qiao, Shanlei; Hu, Nan; Hu, Yanhui; Wu, Wei; Qiu, Lianglin; Zhang, Ruyang; Wang, Yubang; Wang, Shoulin; Zhou, Zuomin; Xia, Yankai; Wang, Xinru

2012-01-01

Background Male reproductive toxicity induced by exposure to bisphenol A (BPA) has been widely reported. The testes have proven to be a major target organ of BPA toxicity, so studying testicular metabolite variation holds promise for the discovery of mechanisms linked to the toxic effects of BPA on reproduction. Methodology/Principal Findings Male Sprague-Dawley rats were orally administered doses of BPA at the levels of 0, 50 mg/kg/d for 8 weeks. We used an unbiased liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomics approach to discover, identify, and analyze the variation of testicular metabolites. Two n-6 fatty acids, linoleic acid (LA) and arachidonic acid (AA) were identified as potential testicular biomarkers. Decreased levels of LA and increased levels of AA as well as AA/LA ratio were observed in the testes of the exposed group. According to these suggestions, testicular antioxidant enzyme levels were detected. Testicular superoxide dismutase (SOD) declined significantly in the exposed group compared with that in the non-exposed group, and the glutathione peroxidase (GSH-Px) as well as catalase (CAT) also showed a decreasing trend in BPA treated group. Conclusions/Significance BPA caused testicular n-6 fatty acid composition variation and decreased antioxidant enzyme levels. This study emphasizes that metabolomics brings the promise of biomarkers identification for the discovery of mechanisms underlying reproductive toxicity. PMID:23024759

Mass spectrometry as a quantitative tool in plant metabolomics

PubMed Central

Jorge, Tiago F.; Mata, Ana T.

2016-01-01

Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided. This article is part of the themed issue ‘Quantitative mass spectrometry’. PMID:27644967

Modern plant metabolomics: Advanced natural product gene discoveries, improved technologies, and future prospects

DOE PAGES

Sumner, Lloyd W.; Lei, Zhentian; Nikolau, Basil J.; ...

2014-10-24

Plant metabolomics has matured and modern plant metabolomics has accelerated gene discoveries and the elucidation of a variety of plant natural product biosynthetic pathways. This study highlights specific examples of the discovery and characterization of novel genes and enzymes associated with the biosynthesis of natural products such as flavonoids, glucosinolates, terpenoids, and alkaloids. Additional examples of the integration of metabolomics with genome-based functional characterizations of plant natural products that are important to modern pharmaceutical technology are also reviewed. This article also provides a substantial review of recent technical advances in mass spectrometry imaging, nuclear magnetic resonance imaging, integrated LC-MS-SPE-NMR formore » metabolite identifications, and x-ray crystallography of microgram quantities for structural determinations. The review closes with a discussion on the future prospects of metabolomics related to crop species and herbal medicine.« less

Multi-platform metabolomics assays for human lung lavage fluids in an air pollution exposure study.

PubMed

Surowiec, Izabella; Karimpour, Masoumeh; Gouveia-Figueira, Sandra; Wu, Junfang; Unosson, Jon; Bosson, Jenny A; Blomberg, Anders; Pourazar, Jamshid; Sandström, Thomas; Behndig, Annelie F; Trygg, Johan; Nording, Malin L

2016-07-01

Metabolomics protocols are used to comprehensively characterize the metabolite content of biological samples by exploiting cutting-edge analytical platforms, such as gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) assays, as well as nuclear magnetic resonance (NMR) assays. We have developed novel sample preparation procedures combined with GC-MS, LC-MS, and NMR metabolomics profiling for analyzing bronchial wash (BW) and bronchoalveolar lavage (BAL) fluid from 15 healthy volunteers following exposure to biodiesel exhaust and filtered air. Our aim was to investigate the responsiveness of metabolite profiles in the human lung to air pollution exposure derived from combustion of biofuels, such as rapeseed methyl ester biodiesel, which are increasingly being promoted as alternatives to conventional fossil fuels. Our multi-platform approach enabled us to detect the greatest number of unique metabolites yet reported in BW and BAL fluid (82 in total). All of the metabolomics assays indicated that the metabolite profiles of the BW and BAL fluids differed appreciably, with 46 metabolites showing significantly different levels in the corresponding lung compartments. Furthermore, the GC-MS assay revealed an effect of biodiesel exhaust exposure on the levels of 1-monostearylglycerol, sucrose, inosine, nonanoic acid, and ethanolamine (in BAL) and pentadecanoic acid (in BW), whereas the LC-MS assay indicated a shift in the levels of niacinamide (in BAL). The NMR assay only identified lactic acid (in BW) as being responsive to biodiesel exhaust exposure. Our findings demonstrate that the proposed multi-platform approach is useful for wide metabolomics screening of BW and BAL fluids and can facilitate elucidation of metabolites responsive to biodiesel exhaust exposure. Graphical Abstract Graphical abstract illustrating the study workflow. NMR Nuclear Magnetic Resonance, LC-TOFMS Liquid chromatography-Time Of Flight Mass Spectrometry, GC Gas Chromatography-Mass spectrometry.

New findings on the in vivo antioxidant activity of Curcuma longa extract by an integrated (1)H NMR and HPLC-MS metabolomic approach.

PubMed

Dall'Acqua, Stefano; Stocchero, Matteo; Boschiero, Irene; Schiavon, Mariano; Golob, Samuel; Uddin, Jalal; Voinovich, Dario; Mammi, Stefano; Schievano, Elisabetta

2016-03-01

Curcuminoids possess powerful antioxidant activity as demonstrated in many chemical in vitro tests and in several in vivo trials. Nevertheless, the mechanism of this activity is not completely elucidated and studies on the in vivo antioxidant effects are still needed. Metabolomics may be used as an attractive approach for such studies and in this paper, we describe the effects of oral administration of a Curcuma longa L. extract (150 mg/kg of total curcuminoids) to 12 healthy rats with particular attention to urinary markers of oxidative stress. The experiment was carried out over 33 days and changes in the 24-h urine samples metabolome were evaluated by (1)H NMR and HPLC-MS. Both techniques produced similar representations for the collected samples confirming our previous study. Modifications of the urinary metabolome lead to the observation of different variables proving the complementarity of (1)H NMR and HPLC-MS for metabolomic purposes. The urinary levels of allantoin, m-tyrosine, 8-hydroxy-2'-deoxyguanosine, and nitrotyrosine were decreased in the treated group thus supporting an in vivo antioxidant effect of the oral administration of Curcuma extract to healthy rats. On the other hand, urinary TMAO levels were higher in the treated compared to the control group suggesting a role of curcumin supplementation on microbiota or on TMAO urinary excretion. Furthermore, the urinary levels of the sulphur containing compounds taurine and cystine were also changed suggesting a role for such constituents in the biochemical pathways involved in Curcuma extract bioactivity and indicating the need for further investigation on the complex role of antioxidant curcumin effects. Copyright © 2015 Elsevier B.V. All rights reserved.

Biomarker analysis of American toad (Anaxyrus americanus) ...

EPA Pesticide Factsheets

The objective of the current study was to use a biomarker-based approach to investigate the influence of atrazine exposure on American toad (Anaxyrus americanus) and grey tree frog (Hyla versicolor) tadpoles. Atrazine is one of the most frequently detected herbicides in environmental matrices throughout the United States. In surface waters, it has been found at concentrations from 0.04–2859 μg/L and thus presents a likely exposure scenario for non-target species such as amphibians. Studies have examined the effect of atrazine on the metamorphic parameters of amphibians, however, the data are often contradictory. Gosner stage 22–24 tadpoles were exposed to 0 (control), 10, 50, 250 or 1250 μg/L of atrazine for 48 h. Endogenous polar metabolites were extracted and analyzed using gas chromatography coupled with mass spectrometry. Statistical analyses of the acquired spectra with machine learning classification models demonstrated identifiable changes in the metabolomic profiles between exposed and control tadpoles. Support vector machine models with recursive feature elimination created a more efficient, non-parametric data analysis and increased interpretability of metabolomic profiles. Biochemical fluxes observed in the exposed groups of both A. americanus and H. versicolor displayed perturbations in a number of classes of biological macromolecules including fatty acids, amino acids, purine nucleosides, pyrimidines, and mono- and di-saccharides. Metabolomic

The Effects of Graded Levels of Calorie Restriction: XIII. Global Metabolomics Screen Reveals Graded Changes in Circulating Amino Acids, Vitamins, and Bile Acids in the Plasma of C57BL/6 Mice.

PubMed

Green, Cara L; Soltow, Quinlyn A; Mitchell, Sharon E; Derous, Davina; Wang, Yingchun; Chen, Luonan; Han, Jing-Dong J; Promislow, Daniel E L; Lusseau, David; Douglas, Alex; Jones, Dean P; Speakman, John R

2018-04-30

Calorie restriction (CR) remains the most robust intervention to extend life span and improve health span. Using a global mass spectrometry-based metabolomics approach, we identified metabolites that were significantly differentially expressed in the plasma of C57BL/6 mice, fed graded levels of calorie restriction (10% CR, 20% CR, 30% CR, and 40% CR) compared with mice fed ad libitum for 12 hours a day. The differential expression of metabolites increased with the severity of CR. Pathway analysis revealed that graded CR had an impact on vitamin E and vitamin B levels, branched chain amino acids, aromatic amino acids, and fatty acid pathways. The majority of amino acids correlated positively with fat-free mass and visceral fat mass, indicating a strong relationship with body composition and vitamin E metabolites correlated with stomach and colon size, which may allude to the beneficial effects of investing in gastrointestinal organs with CR. In addition, metabolites that showed a graded effect, such as the sphinganines, carnitines, and bile acids, match our previous study on liver, which suggests not only that CR remodels the metabolome in a way that promotes energy efficiency, but also that some changes are conserved across tissues.

Evaluation of peak picking quality in LC-MS metabolomics data.

PubMed

Brodsky, Leonid; Moussaieff, Arieh; Shahaf, Nir; Aharoni, Asaph; Rogachev, Ilana

2010-11-15

The output of LC-MS metabolomics experiments consists of mass-peak intensities identified through a peak-picking/alignment procedure. Besides imperfections in biological samples and instrumentation, data accuracy is highly dependent on the applied algorithms and their parameters. Consequently, quality control (QC) is essential for further data analysis. Here, we present a QC approach that is based on discrepancies between replicate samples. First, the quantile normalization of per-sample log-signal distributions is applied to each group of biologically homogeneous samples. Next, the overall quality of each replicate group is characterized by the Z-transformed correlation coefficients between samples. This general QC allows a tuning of the procedure's parameters which minimizes the inter-replicate discrepancies in the generated output. Subsequently, an in-depth QC measure detects local neighborhoods on a template of aligned chromatograms that are enriched by divergences between intensity profiles of replicate samples. These neighborhoods are determined through a segmentation algorithm. The retention time (RT)-m/z positions of the neighborhoods with local divergences are indicative of either: incorrect alignment of chromatographic features, technical problems in the chromatograms, or to a true biological discrepancy between replicates for particular metabolites. We expect this method to aid in the accurate analysis of metabolomics data and in the development of new peak-picking/alignment procedures.

Normalization of metabolomics data with applications to correlation maps.

PubMed

Jauhiainen, Alexandra; Madhu, Basetti; Narita, Masako; Narita, Masashi; Griffiths, John; Tavaré, Simon

2014-08-01

In metabolomics, the goal is to identify and measure the concentrations of different metabolites (small molecules) in a cell or a biological system. The metabolites form an important layer in the complex metabolic network, and the interactions between different metabolites are often of interest. It is crucial to perform proper normalization of metabolomics data, but current methods may not be applicable when estimating interactions in the form of correlations between metabolites. We propose a normalization approach based on a mixed model, with simultaneous estimation of a correlation matrix. We also investigate how the common use of a calibration standard in nuclear magnetic resonance (NMR) experiments affects the estimation of correlations. We show with both real and simulated data that our proposed normalization method is robust and has good performance when discovering true correlations between metabolites. The standardization of NMR data is shown in simulation studies to affect our ability to discover true correlations to a small extent. However, comparing standardized and non-standardized real data does not result in any large differences in correlation estimates. Source code is freely available at https://sourceforge.net/projects/metabnorm/ alexandra.jauhiainen@ki.se Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metabolomic study of corticosterone-induced cytotoxicity in PC12 cells by ultra performance liquid chromatography-quadrupole/time-of-flight mass spectrometry.

PubMed

Zhang, Hongye; Zheng, Hua; Zhao, Gan; Tang, Chaoling; Lu, Shiyin; Cheng, Bang; Wu, Fang; Wei, Jinbin; Liang, Yonghong; Ruan, Junxiang; Song, Hui; Su, Zhiheng

2016-03-01

Glucocorticoids (GCs) have been proved to be an important pathogenic factor of some neuropsychiatric disorders. Usually, a classical injury model based on corticosterone-induced cytotoxicity of differentiated rat pheochromocytoma (PC12) cells was used to stimulate the state of GC damage of hippocampal neurons and investigate its potential mechanisms involved. However, up to now, the mechanism of corticosterone-induced cytotoxicity in PC12 cells was still looking forward to further elucidation. In this work, the metabolomic study of the biochemical changes caused by corticosterone-induced cytotoxicity in differentiated PC12 cells with different corticosterone concentrations was performed for the first time, using the ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF MS). Partial least squares-discriminate analysis (PLS-DA) indicated that metabolic profiles of different corticosterone treatment groups deviated from the control group. A total of fifteen metabolites were characterized as potential biomarkers involved in corticosterone-induced cytotoxicity, which were corresponding to the dysfunctions of five pathways including glycerophospholipid metabolism, sphingolipid metabolism, oxidation of fatty acids, glycerolipid metabolism and sterol lipid metabolism. This study indicated that the rapid and holistic cell metabolomics approach might be a powerful tool to further study the pathogenesis mechanism of corticosterone-induced cytotoxicity in PC12 cells.

Metabolomic Profiles of Aspergillus oryzae and Bacillus amyloliquefaciens During Rice Koji Fermentation.

PubMed

Lee, Da Eun; Lee, Sunmin; Jang, Eun Seok; Shin, Hye Won; Moon, Byoung Seok; Lee, Choong Hwan

2016-06-14

Rice koji, used early in the manufacturing process for many fermented foods, produces diverse metabolites and enzymes during fermentation. Using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), ultrahigh-performance liquid chromatography linear trap quadrupole ion trap tandem mass spectrometry (UHPLC-LTQ-IT-MS/MS), and multivariate analysis we generated the metabolite profiles of rice koji produced by fermentation with Aspergillus oryzae (RK_AO) or Bacillus amyloliquefaciens (RK_BA) for different durations. Two principal components of the metabolomic data distinguished the rice koji samples according to their fermenter species and fermentation time. Several enzymes secreted by the fermenter species, including α-amylase, protease, and β-glucosidase, were assayed to identify differences in expression levels. This approach revealed that carbohydrate metabolism, serine-derived amino acids, and fatty acids were associated with rice koji fermentation by A. oryzae, whereas aromatic and branched chain amino acids, flavonoids, and lysophospholipids were more typical in rice koji fermentation by B. amyloliquefaciens. Antioxidant activity was significantly higher for RK_BA than for RK_AO, as were the abundances of flavonoids, including tricin, tricin glycosides, apigenin glycosides, and chrysoeriol glycosides. In summary, we have used MS-based metabolomics and enzyme activity assays to evaluate the effects of using different microbial species and fermentation times on the nutritional profile of rice koji.

Emerging new strategies for successful metabolite identification in metabolomics

PubMed Central

Bingol, Kerem; Bruschweiler-Li, Lei; Li, Dawei; Zhang, Bo; Xie, Mouzhe; Brüschweiler, Rafael

2016-01-01

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties. PMID:26915807

Salivary and fecal microbiota and metabolome of celiac children under gluten-free diet.

PubMed

De Angelis, Maria; Vannini, Lucia; Di Cagno, Raffaella; Cavallo, Noemi; Minervini, Fabio; Francavilla, Ruggiero; Ercolini, Danilo; Gobbetti, Marco

2016-12-19

Celiac disease (CD) is an inflammatory autoimmune disorder resulting from the combination of genetic predisposition and gluten ingestion. A life-long gluten free diet (GFD) is the only therapeutic approach. Dysbiosis, which can precede the CD pathogenesis and/or persist when subjects are on GFD, is reviewed and discussed. Salivary microbiota and metabolome differed between healthy and celiac children treated under GFD (T-CD) for at least two years. The type of GFD (African- vs Italian-style) modified the microbiota and metabolome of Saharawi T-CD children. Different studies showed bacterial dysbiosis at duodenal and/or fecal level of patients with active untreated CD (U-CD) and T-CD compared to healthy subjects. The ratio of protective anti-inflammatory bacteria such as Lactobacillus-Bifidobacterium to potentially harmful Bacteroides-Enterobacteriaceae was the lowest in U-CD and T-CD children. In agreement with dysbiosis, serum, fecal and urinary metabolome from U-CD and T-CD patients showed altered levels of free amino acids and volatile organic compounds. However, consensus across studies defining specific bacteria and metabolites in U-CD or T-CD patients is still lacking. Future research efforts are required to determine the relationships between CD and oral and intestinal microbiotas to improve the composition of GFD for restoring the gut dysbiosis as a preventative or therapeutic approach for CD. Copyright © 2016 Elsevier B.V. All rights reserved.

Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis.

PubMed

Wang, Xijun; Wang, Huiyu; Zhang, Aihua; Lu, Xin; Sun, Hui; Dong, Hui; Wang, Ping

2012-02-03

The mother and lateral root of Aconitum carmichaelii Debx, named "Chuanwu" (CW) and "Fuzi", respectively, has been used to relieve joint pain and treat rheumatic diseases for over 2000 years. However, it has a very narrow therapeutic range, and the toxicological risk of its usage remains very high. The traditional Chinese processing approach, Paozhi (detoxifying measure),can decompose poisonous Aconitum alkaloids into less or nontoxic derivatives and plays an important role in detoxification. The difference in metabolomic characters among the crude and processed preparations is still unclear, limited by the lack of sensitive and reliable biomarkers. Therefore, this paper was designed to investigate comprehensive metabolomic characters of the crude and its processed products by UPLC-Q-TOF-HDMS combined with pattern recognition methods and ingenuity pathway analysis (IPA). The significant difference in metabolic profiles and changes of metabolite biomarkers of interest between the crude and processed preparations were well observed. The underlying regulations of Paozhi-perturbed metabolic pathways are discussed according to the identified metabolites, and four metabolic pathways are identified using IPA. The present study demonstrates that metabolomic analysis could greatly facilitate and provide useful information to further comprehensively understand the pharmacological activity and potential toxicity of processed Aconite roots in the clinic.

Untargeted metabolomics unravels functionalities of phosphorylation sites in Saccharomyces cerevisiae.

PubMed

Raguz Nakic, Zrinka; Seisenbacher, Gerhard; Posas, Francesc; Sauer, Uwe

2016-11-15

Coordinated through a complex network of kinases and phosphatases, protein phosphorylation regulates essentially all cellular processes in eukaryotes. Recent advances in proteomics enable detection of thousands of phosphorylation sites (phosphosites) in single experiments. However, functionality of the vast majority of these sites remains unclear and we lack suitable approaches to evaluate functional relevance at a pace that matches their detection. Here, we assess functionality of 26 phosphosites by introducing phosphodeletion and phosphomimic mutations in 25 metabolic enzymes and regulators from the TOR and HOG signaling pathway in Saccharomyces cerevisiae by phenotypic analysis and untargeted metabolomics. We show that metabolomics largely outperforms growth analysis and recovers 10 out of the 13 previously characterized phosphosites and suggests functionality for several novel sites, including S79 on the TOR regulatory protein Tip41. We analyze metabolic profiles to identify consequences underlying regulatory phosphorylation events and detecting glycerol metabolism to have a so far unknown influence on arginine metabolism via phosphoregulation of the glycerol dehydrogenases. Further, we also find S508 in the MAPKK Pbs2 as a potential link for cross-talking between HOG signaling and the cell wall integrity pathway. We demonstrate that metabolic profiles can be exploited for gaining insight into regulatory consequences and biological roles of phosphosites. Altogether, untargeted metabolomics is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of phosphosites.

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study.

PubMed

Li, Yanyun; Chen, Minjian; Liu, Cuiping; Xia, Yankai; Xu, Bo; Hu, Yanhui; Chen, Ting; Shen, Meiping; Tang, Wei

2018-05-01

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

METABOLOMICS FOR DEVELOPING MARKERS OF CHEMICAL EXPOSURE AND DISTINGUISHING TOXICITY PATHWAYS

EPA Science Inventory

Metabolomics involves the application of advanced analytical and statistical tools to profile changes in levels of endogenous metabolites in tissues and biofluids resulting from disease onset, stress, or chemical exposure. Nuclear Magnetic Resonance (NMR) spectroscopy-based meta...

Conventional and accelerated-solvent extractions of green tea (camellia sinensis) for metabolomics-based chemometrics.

PubMed

Kellogg, Joshua J; Wallace, Emily D; Graf, Tyler N; Oberlies, Nicholas H; Cech, Nadja B

2017-10-25

Metabolomics has emerged as an important analytical technique for multiple applications. The value of information obtained from metabolomics analysis depends on the degree to which the entire metabolome is present and the reliability of sample treatment to ensure reproducibility across the study. The purpose of this study was to compare methods of preparing complex botanical extract samples prior to metabolomics profiling. Two extraction methodologies, accelerated solvent extraction and a conventional solvent maceration, were compared using commercial green tea [Camellia sinensis (L.) Kuntze (Theaceae)] products as a test case. The accelerated solvent protocol was first evaluated to ascertain critical factors influencing extraction using a D-optimal experimental design study. The accelerated solvent and conventional extraction methods yielded similar metabolite profiles for the green tea samples studied. The accelerated solvent extraction yielded higher total amounts of extracted catechins, was more reproducible, and required less active bench time to prepare the samples. This study demonstrates the effectiveness of accelerated solvent as an efficient methodology for metabolomics studies. Copyright © 2017. Published by Elsevier B.V.

Data standards can boost metabolomics research, and if there is a will, there is a way.

PubMed

Rocca-Serra, Philippe; Salek, Reza M; Arita, Masanori; Correa, Elon; Dayalan, Saravanan; Gonzalez-Beltran, Alejandra; Ebbels, Tim; Goodacre, Royston; Hastings, Janna; Haug, Kenneth; Koulman, Albert; Nikolski, Macha; Oresic, Matej; Sansone, Susanna-Assunta; Schober, Daniel; Smith, James; Steinbeck, Christoph; Viant, Mark R; Neumann, Steffen

2016-01-01

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

The effects of age and dietary restriction on the tissue-specific metabolome of Drosophila.

PubMed

Laye, Matthew J; Tran, ViLinh; Jones, Dean P; Kapahi, Pankaj; Promislow, Daniel E L

2015-10-01

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age-related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease-associated phenotypes. Here, we use high-resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient-rich ad libitum (AL) or nutrient-restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age-related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age. © 2015 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Metabolomic Fingerprint of Heart Failure with Preserved Ejection Fraction

PubMed Central

Zordoky, Beshay N.; Sung, Miranda M.; Ezekowitz, Justin; Mandal, Rupasri; Han, Beomsoo; Bjorndahl, Trent C.; Bouatra, Souhaila; Anderson, Todd; Oudit, Gavin Y.; Wishart, David S.; Dyck, Jason R. B.

2015-01-01

Background Heart failure (HF) with preserved ejection fraction (HFpEF) is increasingly recognized as an important clinical entity. Preclinical studies have shown differences in the pathophysiology between HFpEF and HF with reduced ejection fraction (HFrEF). Therefore, we hypothesized that a systematic metabolomic analysis would reveal a novel metabolomic fingerprint of HFpEF that will help understand its pathophysiology and assist in establishing new biomarkers for its diagnosis. Methods and Results Ambulatory patients with clinical diagnosis of HFpEF (n = 24), HFrEF (n = 20), and age-matched non-HF controls (n = 38) were selected for metabolomic analysis as part of the Alberta HEART (Heart Failure Etiology and Analysis Research Team) project. 181 serum metabolites were quantified by LC-MS/MS and 1H-NMR spectroscopy. Compared to non-HF control, HFpEF patients demonstrated higher serum concentrations of acylcarnitines, carnitine, creatinine, betaine, and amino acids; and lower levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins. Medium and long-chain acylcarnitines and ketone bodies were higher in HFpEF than HFrEF patients. Using logistic regression, two panels of metabolites were identified that can separate HFpEF patients from both non-HF controls and HFrEF patients with area under the receiver operating characteristic (ROC) curves of 0.942 and 0.981, respectively. Conclusions The metabolomics approach employed in this study identified a unique metabolomic fingerprint of HFpEF that is distinct from that of HFrEF. This metabolomic fingerprint has been utilized to identify two novel panels of metabolites that can separate HFpEF patients from both non-HF controls and HFrEF patients. Clinical Trial Registration ClinicalTrials.gov NCT02052804 PMID:26010610

Monitoring Ecological Impacts of Environmental Surface ...

EPA Pesticide Factsheets

Optimized cell-based metabolomics has been used to study the impacts of contaminants in surface waters on human and fish metabolomes. This method has proven to be resource- and time-effective, as well as sustainable for long term and large scale studies. In the current study, cell-based metabolomics is used to investigate the impacts of contaminants in surface waters on biological pathways in human and ecologically relevant cell lines. Water samples were collected from stream sites nationwide, where significant impacts have been estimated from the most potentially contaminated sources (i.e. waste water treatment plants, concentrated animal feeding operations, mining operations, and plant-based agricultural operations that use intensive chemical applications). Zebrafish liver cells (ZFL) were used to study exposure impacts on in vitro metabolomes. In addition, a small number of water samples were studied using two human cell lines (liver cells, HepG2 and brain cells, LN229). The cellular metabolites were profiled by nuclear magnetic resonance (NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Detailed methods and results will be reported. Presented at SETAC North America 37th Annual Meeting

Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma

PubMed Central

Wang, Hanghang; Muehlbauer, Michael J.; O’Neal, Sara K.; Newgard, Christopher B.; Hauser, Elizabeth R.; Shah, Svati H.

2017-01-01

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a practical application of method validation. Specifically, we sought to identify and modify steps in gas chromatography-mass spectrometry (GC-MS)-based, non-targeted metabolomic profiling of human plasma that could influence metabolite identification and quantification. Our experimental design included two studies: (1) a limiting-dilution study, which investigated the effects of dilution on analyte identification and quantification; and (2) a concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We confirmed that contaminants, concentration, repeatability and intermediate precision are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized to provide recommendations for experimental design of GC-MS-based non-targeted profiling of human plasma. PMID:28841195

Untargeted mass spectrometry-based metabolomic profiling of pleural effusions: fatty acids as novel cancer biomarkers for malignant pleural effusions.

PubMed

Lam, Ching-Wan; Law, Chun-Yiu

2014-09-05

Untargeted mass spectrometry-based metabolomic profiling is a powerful analytical method used for broad-spectrum identification and quantification of metabolites in biofluids in human health and disease states. In this study, we exploit metabolomic profiling for cancer biomarker discovery for diagnosis of malignant pleural effusions. We envisage the result will be clinically useful since currently there are no cancer biomarkers that are accurate enough for the diagnosis of malignant pleural effusions. Metabolomes of 32 malignant pleural effusions from lung cancer patients and 18 benign effusions from patients with pulmonary tuberculosis were analyzed using reversed-phase liquid chromatography tandem mass spectrometry (LC-MS/MS) using AB SCIEX TripleTOF 5600. MS spectra were analyzed using XCMS, PeakView, and LipidView. Metabolome-Wide Association Study (MWAS) was performed by Receiver Operating Characteristic Curve Explorer and Tester (ROCCET). Insignificant markers were filtered out using a metabolome-wide significance level (MWSL) with p-value < 2 × 10(-5) for t test. Only compounds in Human Metabolome Database (HMDB) will be used as cancer biomarkers. ROCCET analysis of ESI positive and negative MS spectra revealed free fatty acid (FFA) 18:1 (oleic acid) had the largest area-under-ROC of 0.96 (95% CI = 0.87-1.00) in malignant pleural effusions. Using a ratio of FFA 18:1-to-ceramide (d18:1/16:0), the area-under-ROC was further increased to 0.99 (95% CI = 0.91-1.00) with sensitivity 93.8% and specificity 100.0%. Using untargeted metabolomic profiling, the diagnostic cancer biomarker with the largest area-under-ROC can be determined objectively. This lipogenic phenotype could be explained by overexpression of fatty acid synthase (FASN) in cancer cells. The diagnostic performance of FFA 18:1-to-ceramide (d18:1/16:0) ratio supports its use for diagnosis of malignant pleural effusions.

Holistic Analysis Enhances the Description of Metabolic Complexity in Dietary Natural Products1234

PubMed Central

Kulakowski, Daniel; Lankin, David C; McAlpine, James B; Chen, Shao-Nong

2016-01-01

In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic analysis of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concentrations. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its composition, thereby leading to an untargeted analysis. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR–based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented. PMID:27180381

A novel in vitro metabolomics approach for neurotoxicity testing, proof of principle for methyl mercury chloride and caffeine.

PubMed

van Vliet, Erwin; Morath, Siegfried; Eskes, Chantra; Linge, Jens; Rappsilber, Juri; Honegger, Paul; Hartung, Thomas; Coecke, Sandra

2008-01-01

There is a need for more efficient methods giving insight into the complex mechanisms of neurotoxicity. Testing strategies including in vitro methods have been proposed to comply with this requirement. With the present study we aimed to develop a novel in vitro approach which mimics in vivo complexity, detects neurotoxicity comprehensively, and provides mechanistic insight. For this purpose we combined rat primary re-aggregating brain cell cultures with a mass spectrometry (MS)-based metabolomics approach. For the proof of principle we treated developing re-aggregating brain cell cultures for 48 h with the neurotoxicant methyl mercury chloride (0.1-100 microM) and the brain stimulant caffeine (1-100 microM) and acquired cellular metabolic profiles. To detect toxicant-induced metabolic alterations the profiles were analysed using commercial software which revealed patterns in the multi-parametric dataset by principal component analyses (PCA), and recognised the most significantly altered metabolites. PCA revealed concentration-dependent cluster formations for methyl mercury chloride (0.1-1 microM), and treatment-dependent cluster formations for caffeine (1-100 microM) at sub-cytotoxic concentrations. Four relevant metabolites responsible for the concentration-dependent alterations following methyl mercury chloride treatment could be identified using MS-MS fragmentation analysis. These were gamma-aminobutyric acid, choline, glutamine, creatine and spermine. Their respective mass ion intensities demonstrated metabolic alterations in line with the literature and suggest that the metabolites could be biomarkers for mechanisms of neurotoxicity or neuroprotection. In addition, we evaluated whether the approach could identify neurotoxic potential by testing eight compounds which have target organ toxicity in the liver, kidney or brain at sub-cytotoxic concentrations. PCA revealed cluster formations largely dependent on target organ toxicity indicating possible potential for the development of a neurotoxicity prediction model. With such results it could be useful to perform a validation study to determine the reliability, relevance and applicability of this approach to neurotoxicity screening. Thus, for the first time we show the benefits and utility of in vitro metabolomics to comprehensively detect neurotoxicity and to discover new biomarkers.

RAMAN SPECTROSCOPY-BASED METABOLOMICS: EVALUATION OF SAMPLE PREPARATION AND OPTICAL ACCESSORIES

EPA Science Inventory

The field of metabonomics/metabolomics involves observing endogenous metabolites from organisms that change in response to exposure to a stressor or chemical of interest. Methods are being developed for measuring the Raman spectra of low-concentration metabolites in urine. The ...

CHARACTERIZATION OF METABOLITES IN SMALL FISH BIOFLUIDS AND TISSUES BY NMR SPECTROSCOPY

EPA Science Inventory

Nuclear magnetic resonance (NMR) spectroscopy has been utilized for assessing ecotoxicity in small fish models by means of metabolomics. Two fundamental challenges of NMR-based metabolomics are the detection limit and characterization of metabolites (or NMR resonance assignments...

RAMAN SPECTROSCOPY-BASED METABOLOMICS FOR DIFFERENTIATING EXPOSURES TO TRIAZOLE FUNGICIDES USING RAT URINE

EPA Science Inventory

Normal Raman spectroscopy was evaluated as a metabolomic tool for assessing the impacts of exposure to environmental contaminants, using rat urine collected during the course of a toxicological study. Specifically, one of three triazole fungicides, myclobutanil, propiconazole or ...

A (1)H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various Oryza sativa L. Cultivars.

PubMed

Song, Eun-Hye; Kim, Hyun-Ju; Jeong, Jaesik; Chung, Hyun-Jung; Kim, Han-Yong; Bang, Eunjung; Hong, Young-Shick

2016-04-20

Rice grain metabolites are important for better understanding of the plant physiology of various rice cultivars and thus for developing rice cultivars aimed at providing diverse processed products. However, the variation of global metabolites in rice grains has rarely been explored. Here, we report the identification of intra- or intercellular metabolites in rice (Oryza sativa L.) grain powder using a (1)H high-resolution magic angle spinning (HR-MAS) NMR-based metabolomic approach. Compared with nonwaxy rice cultivars, marked accumulation of lipid metabolites such as fatty acids, phospholipids, and glycerophosphocholine in the grains of waxy rice cultivars demonstrated the distinct metabolic regulation and adaptation of each cultivar for effective growth during future germination, which may be reflected by high levels of glutamate, aspartate, asparagine, alanine, and sucrose. Therefore, this study provides important insights into the metabolic variations of diverse rice cultivars and their associations with environmental conditions and genetic backgrounds, with the aim of facilitating efficient development and the improvement of rice grain quality through inbreeding with genetic or chemical modification and mutation.

Spotlight on environmental omics and toxicology: a long way in a short time.

PubMed

Martyniuk, Christopher J; Simmons, Denina B

2016-09-01

The applications for high throughput omics technologies in environmental science have increased dramatically in recent years. Transcriptomics, proteomics, and metabolomics have been used to study how chemicals in our environment affect both aquatic and terrestrial organisms, and the characterization of molecular initiating events is a significant goal in toxicology to better predict adverse responses to toxicants. This special journal edition demonstrates the scope of the science that leverages omics-based methods in both laboratory and wild populations within the context of environmental toxicology, ranging from fish to mammals. It is important to recognize that the environment comprises one axis of the One Health concept - the idea that human health is unequivocally intertwined to our environment and to the organisms that inhabit that environment. We have much to learn from a comparative approach, and studies that integrate the transcriptome, proteome, and the metabolome are expected to offer the most detailed mechanism-based adverse outcome pathways that are applicable for use in both environmental monitoring and risk assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

Expanding the chemical space for natural products by Aspergillus-Streptomyces co-cultivation and biotransformation

PubMed Central

Wu, Changsheng; Zacchetti, Boris; Ram, Arthur F.J.; van Wezel, Gilles P.; Claessen, Dennis; Hae Choi, Young

2015-01-01

Actinomycetes and filamentous fungi produce a wide range of bioactive compounds, with applications as antimicrobials, anticancer agents or agrochemicals. Their genomes contain a far larger number of gene clusters for natural products than originally anticipated, and novel approaches are required to exploit this potential reservoir of new drugs. Here, we show that co-cultivation of the filamentous model microbes Streptomyces coelicolor and Aspergillus niger has a major impact on their secondary metabolism. NMR-based metabolomics combined with multivariate data analysis revealed several compounds that correlated specifically to co-cultures, including the cyclic dipeptide cyclo(Phe-Phe) and 2-hydroxyphenylacetic acid, both of which were produced by A. niger in response to S. coelicolor. Furthermore, biotransformation studies with o-coumaric acid and caffeic acid resulted in the production of the novel compounds (E)-2-(3-hydroxyprop-1-en-1-yl)-phenol and (2E,4E)-3-(2-carboxy-1-hydroxyethyl)-2,4-hexadienedioxic acid, respectively. This highlights the utility of microbial co-cultivation combined with NMR-based metabolomics as an efficient pipeline for the discovery of novel natural products. PMID:26040782