Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

International Nuclear Information System (INIS)

Shen, Weifeng; Han, Wei; Li, Yunong; Meng, Zhiqi; Cai, Leiming; Li, Liang

2016-01-01

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential "1"2C-/"1"3C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N

Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Energy Technology Data Exchange (ETDEWEB)

Shen, Weifeng [Key Laboratory of Detection for Pesticide Residues, Ministry of Agriculture (China); Sericultural Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou (China); Han, Wei; Li, Yunong [Department of Chemistry, University of Alberta, Edmonton, Alberta (Canada); Meng, Zhiqi [Sericultural Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou (China); Cai, Leiming, E-mail: cailm@mail.zaas.ac.cn [Institute of Quality and Standard for Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou (China); Li, Liang, E-mail: Liang.Li@ualberta.ca [Department of Chemistry, University of Alberta, Edmonton, Alberta (Canada)

2016-10-26

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential {sup 12}C-/{sup 13}C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N

Metabolomic imaging of prostate cancer with magnetic resonance spectroscopy and mass spectrometry

International Nuclear Information System (INIS)

Spur, Eva-Margarete; Decelle, Emily A.; Cheng, Leo L.

2013-01-01

Metabolomic imaging of prostate cancer (PCa) aims to improve in vivo imaging capability so that PCa tumors can be localized noninvasively to guide biopsy and evaluated for aggressiveness prior to prostatectomy, as well as to assess and monitor PCa growth in patients with asymptomatic PCa newly diagnosed by biopsy. Metabolomics studies global variations of metabolites with which malignancy conditions can be evaluated by profiling the entire measurable metabolome, instead of focusing only on certain metabolites or isolated metabolic pathways. At present, PCa metabolomics is mainly studied by magnetic resonance spectroscopy (MRS) and mass spectrometry (MS). With MRS imaging, the anatomic image, obtained from magnetic resonance imaging, is mapped with values of disease condition-specific metabolomic profiles calculated from MRS of each location. For example, imaging of removed whole prostates has demonstrated the ability of metabolomic profiles to differentiate cancerous foci from histologically benign regions. Additionally, MS metabolomic imaging of prostate biopsies has uncovered metabolomic expression patterns that could discriminate between PCa and benign tissue. Metabolomic imaging offers the potential to identify cancer lesions to guide prostate biopsy and evaluate PCa aggressiveness noninvasively in vivo, or ex vivo to increase the power of pathology analysis. Potentially, this imaging ability could be applied not only to PCa, but also to different tissues and organs to evaluate other human malignancies and metabolic diseases. (orig.)

Metabolomic imaging of prostate cancer with magnetic resonance spectroscopy and mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Spur, Eva-Margarete [Massachusetts General Hospital, Harvard Medical School, Department of Pathology, Boston, MA (United States); Massachusetts General Hospital, Harvard Medical School, Department of Radiology, Boston, MA (United States); Charite Universitaetsmedizin, Berlin (Germany); Decelle, Emily A.; Cheng, Leo L. [Massachusetts General Hospital, Harvard Medical School, Department of Pathology, Boston, MA (United States); Massachusetts General Hospital, Harvard Medical School, Department of Radiology, Boston, MA (United States)

2013-07-15

Metabolomic imaging of prostate cancer (PCa) aims to improve in vivo imaging capability so that PCa tumors can be localized noninvasively to guide biopsy and evaluated for aggressiveness prior to prostatectomy, as well as to assess and monitor PCa growth in patients with asymptomatic PCa newly diagnosed by biopsy. Metabolomics studies global variations of metabolites with which malignancy conditions can be evaluated by profiling the entire measurable metabolome, instead of focusing only on certain metabolites or isolated metabolic pathways. At present, PCa metabolomics is mainly studied by magnetic resonance spectroscopy (MRS) and mass spectrometry (MS). With MRS imaging, the anatomic image, obtained from magnetic resonance imaging, is mapped with values of disease condition-specific metabolomic profiles calculated from MRS of each location. For example, imaging of removed whole prostates has demonstrated the ability of metabolomic profiles to differentiate cancerous foci from histologically benign regions. Additionally, MS metabolomic imaging of prostate biopsies has uncovered metabolomic expression patterns that could discriminate between PCa and benign tissue. Metabolomic imaging offers the potential to identify cancer lesions to guide prostate biopsy and evaluate PCa aggressiveness noninvasively in vivo, or ex vivo to increase the power of pathology analysis. Potentially, this imaging ability could be applied not only to PCa, but also to different tissues and organs to evaluate other human malignancies and metabolic diseases. (orig.)

Vinegar Metabolomics: An Explorative Study of Commercial Balsamic Vinegars Using Gas Chromatography-Mass Spectrometry

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Farhana R. Pinu

2016-07-01

Full Text Available Balsamic vinegar is a popular food condiment produced from cooked grape must by two successive fermentation (anaerobic and aerobic processes. Although many studies have been performed to determine the composition of major metabolites, including sugars and aroma compounds, no study has been undertaken yet to characterize the comprehensive metabolite composition of balsamic vinegars. Here, we present the first metabolomics study of commercial balsamic vinegars by gas chromatography coupled to mass spectrometry (GC-MS. The combination of three GC-MS methods allowed us to detect >1500 features in vinegar samples, of which 123 metabolites were accurately identified, including 25 amino acids, 26 carboxylic acids, 13 sugars and sugar alcohols, four fatty acids, one vitamin, one tripeptide and over 47 aroma compounds. Moreover, we identified for the first time in vinegar five volatile metabolites: acetin, 2-methylpyrazine, 2-acetyl-1-pyroline, 4-anisidine and 1,3-diacetoxypropane. Therefore, we demonstrated the capability of metabolomics for detecting and identifying large number of metabolites and some of them could be used to distinguish vinegar samples based on their origin and potentially quality.

Mass spectrometry in plant metabolomics strategies: from analytical platforms to data acquisition and processing.

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Ernst, Madeleine; Silva, Denise Brentan; Silva, Ricardo Roberto; Vêncio, Ricardo Z N; Lopes, Norberto Peporine

2014-06-01

Covering: up to 2013. Plant metabolomics is a relatively recent research field that has gained increasing interest in the past few years. Up to the present day numerous review articles and guide books on the subject have been published. This review article focuses on the current applications and limitations of the modern mass spectrometry techniques, especially in combination with electrospray ionisation (ESI), an ionisation method which is most commonly applied in metabolomics studies. As a possible alternative to ESI, perspectives on matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) in metabolomics studies are introduced, a method which still is not widespread in the field. In metabolomics studies the results must always be interpreted in the context of the applied sampling procedures as well as data analysis. Different sampling strategies are introduced and the importance of data analysis is illustrated in the example of metabolic network modelling.

Clinical Metabolomics and Glaucoma.

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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.

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

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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.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

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Ghaste, Manoj; Mistrik, Robert; Shulaev, Vladimir

2016-01-01

Metabolomics, along with other “omics” approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data. PMID:27231903

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

DEFF Research Database (Denmark)

Højer-Pedersen, Jesper Juul; Smedsgaard, Jørn; Nielsen, Jens

2008-01-01

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited...... for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination......, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological...

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

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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

Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders.

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Luan, Hemi; Wang, Xian; Cai, Zongwei

2017-11-12

Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

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Ina Aretz

2016-04-01

Full Text Available Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology.

Science.gov (United States)

Aretz, Ina; Meierhofer, David

2016-04-27

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Informatics for Metabolomics.

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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.

Analytical methods in untargeted metabolomics: state of the art in 2015

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Arnald eAlonso

2015-03-01

Full Text Available Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile –the metabolome- has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance (NMR and mass spectrometry (MS are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high throughput biotechnologies is also reviewed.

Capillary electrophoresis - Mass spectrometry metabolomics analysis revealed enrichment of hypotaurine in rat glioma tissues.

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Gao, Peng; Ji, Min; Fang, Xueyan; Liu, Yingyang; Yu, Zhigang; Cao, Yunfeng; Sun, Aijun; Zhao, Liang; Zhang, Yong

2017-11-15

Glioma is one of the most lethal brain malignancies with unknown etiologies. Many metabolomics analysis aiming at diverse kinds of samples had been performed. Due to the varied adopted analytical platforms, the reported disease-related metabolites were not consistent across different studies. Comparable metabolomics results are more likely to be acquired by analyzing the same sample types with identical analytical platform. For tumor researches, tissue samples metabolomics analysis own the unique advantage that it can gain more direct insight into disease-specific pathological molecules. In this light, a previous reported capillary electrophoresis - mass spectrometry human tissues metabolomics analysis method was employed to profile the metabolome of rat C6 cell implantation gliomas and the corresponding precancerous tissues. It was found that 9 metabolites increased in the glioma tissues. Of them, hypotaurine was the only metabolite that enriched in the malignant tissues as what had been reported in the relevant human tissues metabolomics analysis. Furthermore, hypotaurine was also proved to inhibit α-ketoglutarate-dependent dioxygenases (2-KDDs) through immunocytochemistry staining and in vitro enzymatic activity assays by using C6 cell cultures. This study reinforced the previous conclusion that hypotaurine acted as a competitive inhibitor of 2-KDDs and proved the value of metabolomics in oncology studies. Copyright © 2017. Published by Elsevier Inc.

MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data.

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Brigitte Wägele

Full Text Available Systems Biology is a field in biological science that focuses on the combination of several or all "omics"-approaches in order to find out how genes, transcripts, proteins and metabolites act together in the network of life. Metabolomics as analog to genomics, transcriptomics and proteomics is more and more integrated into biological studies and often transcriptomic and metabolomic experiments are combined in one setup. At a first glance both data types seem to be completely different, but both produce information on biological entities, either transcripts or metabolites. Both types can be overlaid on metabolic pathways to obtain biological information on the studied system. For the joint analysis of both data types the MassTRIX webserver was updated. MassTRIX is freely available at www.masstrix.org.

Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry.

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Boudah, Samia; Olivier, Marie-Françoise; Aros-Calt, Sandrine; Oliveira, Lydie; Fenaille, François; Tabet, Jean-Claude; Junot, Christophe

2014-09-01

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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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.

Best-Matched Internal Standard Normalization in Liquid Chromatography-Mass Spectrometry Metabolomics Applied to Environmental Samples.

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Boysen, Angela K; Heal, Katherine R; Carlson, Laura T; Ingalls, Anitra E

2018-01-16

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization .

Sample normalization methods in quantitative metabolomics.

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Wu, Yiman; Li, Liang

2016-01-22

To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts. Copyright © 2015 Elsevier B.V. All rights reserved.

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

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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 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.

Qualitative metabolome analysis of human cerebrospinal fluid by 13C-/12C-isotope dansylation labeling combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry.

Science.gov (United States)

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by (13)C-dansyl and (12)C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner. © American Society for Mass Spectrometry, 2011

Qualitative Metabolome Analysis of Human Cerebrospinal Fluid by 13C-/12C-Isotope Dansylation Labeling Combined with Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Science.gov (United States)

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by 13C-dansyl and 12C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner.

Mass Spectrometry Strategies for Clinical Metabolomics and Lipidomics in Psychiatry, Neurology, and Neuro-Oncology

Science.gov (United States)

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

Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology.

Science.gov (United States)

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.

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

Science.gov (United States)

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

New approaches for metabolomics by mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Vertes, Akos [George Washington Univ., Washington, DC (United States)

2017-07-10

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 do 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

MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra

Science.gov (United States)

2015-01-01

Systematic analysis and interpretation of the large number of tandem mass spectra (MS/MS) obtained in metabolomics experiments is a bottleneck in discovery-driven research. MS/MS mass spectral libraries are small compared to all known small molecule structures and are often not freely available. MS2Analyzer was therefore developed to enable user-defined searches of thousands of spectra for mass spectral features such as neutral losses, m/z differences, and product and precursor ions from MS/MS spectra in MSP/MGF files. The software is freely available at http://fiehnlab.ucdavis.edu/projects/MS2Analyzer/. As the reference query set, 147 literature-reported neutral losses and their corresponding substructures were collected. This set was tested for accuracy of linking neutral loss analysis to substructure annotations using 19 329 accurate mass tandem mass spectra of structurally known compounds from the NIST11 MS/MS library. Validation studies showed that 92.1 ± 6.4% of 13 typical neutral losses such as acetylations, cysteine conjugates, or glycosylations are correct annotating the associated substructures, while the absence of mass spectra features does not necessarily imply the absence of such substructures. Use of this tool has been successfully demonstrated for complex lipids in microalgae. PMID:25263576

Metabolomics: A Primer.

Science.gov (United States)

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.

Mass Spectra-Based Framework for Automated Structural Elucidation of Metabolome Data to Explore Phytochemical Diversity

Science.gov (United States)

Matsuda, Fumio; Nakabayashi, Ryo; Sawada, Yuji; Suzuki, Makoto; Hirai, Masami Y.; Kanaya, Shigehiko; Saito, Kazuki

2011-01-01

A novel framework for automated elucidation of metabolite structures in liquid chromatography–mass spectrometer metabolome data was constructed by integrating databases. High-resolution tandem mass spectra data automatically acquired from each metabolite signal were used for database searches. Three distinct databases, KNApSAcK, ReSpect, and the PRIMe standard compound database, were employed for the structural elucidation. The outputs were retrieved using the CAS metabolite identifier for identification and putative annotation. A simple metabolite ontology system was also introduced to attain putative characterization of the metabolite signals. The automated method was applied for the metabolome data sets obtained from the rosette leaves of 20 Arabidopsis accessions. Phenotypic variations in novel Arabidopsis metabolites among these accessions could be investigated using this method. PMID:22645535

Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity

Directory of Open Access Journals (Sweden)

Fumio eMatsuda

2011-08-01

Full Text Available A novel framework for automated elucidation of metabolite structures in liquid chromatography-mass spectrometer (LC-MS metabolome data was constructed by integrating databases. High-resolution tandem mass spectra data automatically acquired from each metabolite signal were used for database searches. Three distinct databases, KNApSAcK, ReSpect, and the PRIMe standard compound database, were employed for the structural elucidation. The outputs were retrieved using the CAS metabolite identifier for identification and putative annotation. A simple metabolite ontology system was also introduced to attain putative characterization of the metabolite signals. The automated method was applied for the metabolome data sets obtained from the rosette leaves of 20 Arabidopsis accessions. Phenotypic variations in novel Arabidopsis metabolites among these accessions could be investigated using this method.

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry

CERN Document Server

Mertens, Bart

2017-01-01

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass ...

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

Science.gov (United States)

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.

Metabolome analysis for discovering biomarkers of gastroenterological cancer.

Science.gov (United States)

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.

The next wave in metabolome analysis

DEFF Research Database (Denmark)

Nielsen, Jens; Oliver, S.

2005-01-01

The metabolome of a cell represents the amplification and integration of signals from other functional genomic levels, such as the transcriptome and the proteome. Although this makes metabolomics a useful tool for the high-throughput analysis of phenotypes, the lack of a direct connection...... to the genome makes it difficult to interpret metabolomic data. Nevertheless, functional genomics has produced examples of the use of metabolomics to elucidate the phenotypes of otherwise silent mutations. Despite several successes, we believe that future metabolomic studies must focus on the accurate...... measurement of the concentrations of unambiguously identified metabolites. The research community must develop databases of metabolite concentrations in cells that are grown in several well-defined conditions if metabolomic data are to be integrated meaningfully with data from the other levels of functional...

Profiling the metabolome changes caused by cranberry procyanidins in plasma of female rats using (1) H NMR and UHPLC-Q-Orbitrap-HRMS global metabolomics approaches.

Science.gov (United States)

Liu, Haiyan; Garrett, Timothy J; Tayyari, Fariba; Gu, Liwei

2015-11-01

The objective was to investigate the metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) using (1) H NMR and UHPLC-Q-Orbitrap-HRMS metabolomics approaches, and to identify the contributing metabolites. Twenty-four female Sprague-Dawley rats were randomly separated into two groups and administered PPCP or partially purified apple procyanidins (PPAP) for three times using a 250 mg extracts/kg body weight dose. Plasma was collected 6 h after the last gavage and analyzed using (1) H NMR and UHPLC-Q-Orbitrap-HRMS. No metabolome difference was observed using (1) H NMR metabolomics approach. However, LC-HRMS metabolomics data show that metabolome in the plasma of female rats administered PPCP differed from those gavaged with PPAP. Eleven metabolites were tentatively identified from a total of 36 discriminant metabolic features based on accurate masses and/or product ion spectra. PPCP caused a greater increase of exogenous metabolites including p-hydroxybenzoic acid, phenol, phenol-sulphate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, and 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide in rat plasma. Furthermore, the plasma level of O-methyl-(-)-epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(hydroxyphenyl)-ϒ-valerolactone-O-sulphate, 4-hydroxydiphenylamine, and peonidin-3-O-hexose were higher in female rats administered with PPAP. The metabolome changes caused by cranberry procyanidins were revealed using an UHPLC-Q-Orbitrap-HRMS global metabolomics approach. Exogenous and microbial metabolites were the major identified discriminate biomarkers. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Profiling the Metabolome Changes Caused by Cranberry Procyanidins in Plasma of Female Rats using 1H NMR and UHPLC-Q-Orbitrap-HRMS Global Metabolomics Approaches

Science.gov (United States)

Liu, Haiyan; Garrett, Timothy J.; Tayyari, Fariba; Gu, Liwei

2015-01-01

Scope The objective was to investigate the metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) using 1H NMR and UHPLC-Q-Orbitrap-HRMS metabolomics approaches, and to identify the contributing metabolites. Methods and results Twenty four female Sprague-Dawley rats were randomly separated into two groups and administered PPCP or partially purified apple procyanidins (PPAP) for 3 times using a 250 mg extracts/kg body weight dose. Plasma were collected six hours after the last gavage and analyzed using 1H NMR and UHPLC-Q-Orbitrap-HRMS. No metabolome difference was observed using 1H NMR metabolomics approach. However, LC-HRMS metabolomics data show that metabolome in plasma of female rats administered PPCP differed from those gavaged with PPAP. Eleven metabolites were tentatively identified from a total of 36 discriminant metabolic features based on accurate masses and/or product ion spectra. PPCP caused a greater increase of exogenous metabolites including p-hydroxybenzoic acid, phenol, phenol-sulfate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, and 4′-O-methyl-(−)-epicatechin-3′-O-beta-glucuronide in rat plasma. Furthermore, the plasma level of O-methyl-(−)-epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(hydroxyphenyl)-γ-valerolactone-O-sulphate, 4-hydroxydiphenylamine, and peonidin-3-O-hexose were higher in female rats administered with PPAP. Conclusion The metabolome changes caused by cranberry procyanidins were revealed using an UHPLC-Q-Orbitrap-HRMS global metabolomics approach. Exogenous and microbial metabolites were the major identified discriminate biomarkers. PMID:26264887

Metabolomics, peptidomics and proteomics applications of capillary electrophoresis-mass spectrometry in Foodomics: A review

International Nuclear Information System (INIS)

Ibáñez, Clara; Simó, Carolina; García-Cañas, Virginia; Cifuentes, Alejandro; Castro-Puyana, María

2013-01-01

Graphical abstract: -- Highlights: •Foodomics allows studying food and nutrition through the application of advanced omics approaches. •CE-MS plays a crucial role as analytical platform to carry out omics studies. •CE-MS applications for food metabolomics, proteomics and peptidomics are presented. -- Abstract: In the current post-genomic era, Foodomics has been defined as a discipline that studies food and nutrition through the application of advanced omics approaches. Foodomics involves the use of genomics, transcriptomics, epigenetics, proteomics, peptidomics, and/or metabolomics to investigate food quality, safety, traceability and bioactivity. In this context, capillary electrophoresis-mass spectrometry (CE-MS) has been applied mainly in food proteomics, peptidomics and metabolomics. The aim of this review work is to present an overview of the most recent developments and applications of CE-MS as analytical platform for Foodomics, covering the relevant works published from 2008 to 2012. The review provides also information about the integration of several omics approaches in the new Foodomics field

Metabolomics, peptidomics and proteomics applications of capillary electrophoresis-mass spectrometry in Foodomics: A review

Energy Technology Data Exchange (ETDEWEB)

Ibáñez, Clara; Simó, Carolina; García-Cañas, Virginia; Cifuentes, Alejandro, E-mail: a.cifuentes@csic.es; Castro-Puyana, María

2013-11-13

Graphical abstract: -- Highlights: •Foodomics allows studying food and nutrition through the application of advanced omics approaches. •CE-MS plays a crucial role as analytical platform to carry out omics studies. •CE-MS applications for food metabolomics, proteomics and peptidomics are presented. -- Abstract: In the current post-genomic era, Foodomics has been defined as a discipline that studies food and nutrition through the application of advanced omics approaches. Foodomics involves the use of genomics, transcriptomics, epigenetics, proteomics, peptidomics, and/or metabolomics to investigate food quality, safety, traceability and bioactivity. In this context, capillary electrophoresis-mass spectrometry (CE-MS) has been applied mainly in food proteomics, peptidomics and metabolomics. The aim of this review work is to present an overview of the most recent developments and applications of CE-MS as analytical platform for Foodomics, covering the relevant works published from 2008 to 2012. The review provides also information about the integration of several omics approaches in the new Foodomics field.

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

Bingol, Kerem

2018-04-18

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 NMR 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.

Quantitative metabolomics based on gas chromatography mass spectrometry: Status and perspectives

NARCIS (Netherlands)

Koek, M.M.; Jellema, R.H.; Greef, J. van der; Tas, A.C.; Hankemeier, T.

2011-01-01

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review.

Science.gov (United States)

Dudzik, Danuta; Barbas-Bernardos, Cecilia; García, Antonia; Barbas, Coral

2018-01-05

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study. Copyright © 2017 Elsevier B.V. All rights reserved.

Blood Metabolic Signatures of Body Mass Index: A Targeted Metabolomics Study in the EPIC Cohort.

NARCIS (Netherlands)

Carayol, Marion; Leitzmann, Michael F; Ferrari, Pietro; Zamora-Ros, Raul; Achaintre, David; Stepien, Magdalena; Schmidt, Julie A; Travis, Ruth C; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Kaaks, Rudolf; Kühn, Tilman; Boeing, Heiner; Bachlechner, Ursula; Trichopoulou, Antonia; Bamia, Christina; Palli, Domenico; Agnoli, Claudia; Tumino, Rosario; Vineis, Paolo; Panico, Salvatore; Quirós, J Ramón; Sánchez-Cantalejo, Emilio; Huerta, José María; Ardanaz, Eva; Arriola, Larraitz; Agudo, Antonio; Nilsson, Jan; Melander, Olle; Bueno-de-Mesquita, Bas; Peeters, Petra H; Wareham, Nick; Khaw, Kay-Tee; Jenab, Mazda; Key, Timothy J; Scalbert, Augustin; Rinaldi, Sabina

2017-01-01

Metabolomics is now widely used to characterize metabolic phenotypes associated with lifestyle risk factors such as obesity. The objective of the present study was to explore the associations of body mass index (BMI) with 145 metabolites measured in blood samples in the European Prospective

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal

Energy Technology Data Exchange (ETDEWEB)

Karimpour, Masoumeh; Surowiec, Izabella; Wu, Junfang [Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå (Sweden); Gouveia-Figueira, Sandra [Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå (Sweden); Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå (Sweden); Pinto, Rui [Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå (Sweden); Bioinformatics Infrastructure for Life Sciences (Sweden); Trygg, Johan [Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå (Sweden); Zivkovic, Angela M. [Department of Nutrition, University of California, Davis, One Shields Ave, CA 95616 (United States); Nording, Malin L., E-mail: malin.nording@umu.se [Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 90187 Umeå (Sweden)

2016-02-18

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal

International Nuclear Information System (INIS)

Karimpour, Masoumeh; Surowiec, Izabella; Wu, Junfang; Gouveia-Figueira, Sandra; Pinto, Rui; Trygg, Johan; Zivkovic, Angela M.; Nording, Malin L.

2016-01-01

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a

Can NMR solve some significant challenges in metabolomics?

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Gowda, G.A. Nagana; Raftery, Daniel

2015-01-01

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact biospecimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory. PMID:26476597

Can NMR solve some significant challenges in metabolomics?

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Nagana Gowda, G A; Raftery, Daniel

2015-11-01

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory. Copyright © 2015 Elsevier Inc. All rights reserved.

Can NMR solve some significant challenges in metabolomics?

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Nagana Gowda, G. A.; Raftery, Daniel

2015-11-01

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.

Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics.

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Caesar, Lindsay K; Kvalheim, Olav M; Cech, Nadja B

2018-08-27

Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in

Advances in computational metabolomics and databases deepen the understanding of metabolisms.

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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.

Metabolomic studies in pulmonology

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R. R. Furina

2015-01-01

Full Text Available The review shows the results of metabolomic studies in pulmonology. The key idea of metabolomics is to detect specific biomarkers in a biological sample for the diagnosis of diseases of the bronchi and lung. Main methods for the separation and identification of volatile organic substances as biomarkers (gas chromatography, mass spectrometry, and nuclear magnetic resonance spectrometry used in metabolomics are given. A solid-phase microextraction method used to pre-prepare a sample is also covered. The results of laboratory tests for biomarkers for lung cancer, acute respiratory distress syndrome, chronic obstructive pulmonary disease, cystic fibrosis, chronic infections, and pulmonary tuberculosis are presented. In addition, emphasis is placed on the possibilities of metabolomics used in experimental medicine, including to the study of asthma. The information is of interest to both theorists and practitioners.

Quality assurance of metabolomics.

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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

2015-01-01

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.

Untargeted Metabolomics Strategies—Challenges and Emerging Directions

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Schrimpe-Rutledge, Alexandra C.; Codreanu, Simona G.; Sherrod, Stacy D.; McLean, John A.

2016-12-01

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies—specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.

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

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Carroll Adam J

2010-07-01

Full Text Available Abstract Background 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. Description 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. Conclusions 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

Nanoparticle-Assisted Metabolomics

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Bo Zhang

2018-03-01

Full Text Available Understanding and harnessing the interactions between nanoparticles and biological molecules is at the forefront of applications of nanotechnology to modern biology. Metabolomics has emerged as a prominent player in systems biology as a complement to genomics, transcriptomics and proteomics. Its focus is the systematic study of metabolite identities and concentration changes in living systems. Despite significant progress over the recent past, important challenges in metabolomics remain, such as the deconvolution of the spectra of complex mixtures with strong overlaps, the sensitive detection of metabolites at low abundance, unambiguous identification of known metabolites, structure determination of unknown metabolites and standardized sample preparation for quantitative comparisons. Recent research has demonstrated that some of these challenges can be substantially alleviated with the help of nanoscience. Nanoparticles in particular have found applications in various areas of bioanalytical chemistry and metabolomics. Their chemical surface properties and increased surface-to-volume ratio endows them with a broad range of binding affinities to biomacromolecules and metabolites. The specific interactions of nanoparticles with metabolites or biomacromolecules help, for example, simplify metabolomics spectra, improve the ionization efficiency for mass spectrometry or reveal relationships between spectral signals that belong to the same molecule. Lessons learned from nanoparticle-assisted metabolomics may also benefit other emerging areas, such as nanotoxicity and nanopharmaceutics.

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

NARCIS (Netherlands)

Rojas-Cherto, M.; Peironcely, J.E.; Kasper, P.T.; Hooft, van der J.J.J.; Vos, de R.C.H.; Vreeken, R.; Hankemeier, T.; Reijmers, T.

2012-01-01

Multistage mass spectrometry (MSn) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a

The Human Urine Metabolome

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Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.

2013-01-01

Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing

A Rough Guide to Metabolite Identification Using High Resolution Liquid Chromatography Mass Spectrometry in Metabolomic Profiling in Metazoans

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David G Watson

2013-01-01

Full Text Available Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS2 as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.

Causal Genetic Variation Underlying Metabolome Differences.

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Swain-Lenz, Devjanee; Nikolskiy, Igor; Cheng, Jiye; Sudarsanam, Priya; Nayler, Darcy; Staller, Max V; Cohen, Barak A

2017-08-01

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81 , cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype. Copyright © 2017 by the Genetics Society of America.

Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells.

Science.gov (United States)

Luo, Xian; Li, Liang

2017-11-07

In cellular metabolomics, it is desirable to carry out metabolomic profiling using a small number of cells in order to save time and cost. In some applications (e.g., working with circulating tumor cells in blood), only a limited number of cells are available for analysis. In this report, we describe a method based on high-performance chemical isotope labeling (CIL) nanoflow liquid chromatography mass spectrometry (nanoLC-MS) for high-coverage metabolomic analysis of small numbers of cells (i.e., ≤10000 cells). As an example, 12 C-/ 13 C-dansyl labeling of the metabolites in lysates of 100, 1000, and 10000 MCF-7 breast cancer cells was carried out using a new labeling protocol tailored to handle small amounts of metabolites. Chemical-vapor-assisted ionization in a captivespray interface was optimized for improving metabolite ionization and increasing robustness of nanoLC-MS. Compared to microflow LC-MS, the nanoflow system provided much improved metabolite detectability with a significantly reduced sample amount required for analysis. Experimental duplicate analyses of biological triplicates resulted in the detection of 1620 ± 148, 2091 ± 89 and 2402 ± 80 (n = 6) peak pairs or metabolites in the amine/phenol submetabolome from the 12 C-/ 13 C-dansyl labeled lysates of 100, 1000, and 10000 cells, respectively. About 63-69% of these peak pairs could be either identified using dansyl labeled standard library or mass-matched to chemical structures in human metabolome databases. We envisage the routine applications of this method for high-coverage quantitative cellular metabolomics using a starting material of 10000 cells. Even for analyzing 100 or 1000 cells, although the metabolomic coverage is reduced from the maximal coverage, this method can still detect thousands of metabolites, allowing the analysis of a large fraction of the metabolome and focused analysis of the detectable metabolites.

Single cell metabolomics

NARCIS (Netherlands)

Heinemann, Matthias; Zenobi, Renato

Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single

Characterization and Discrimination of Ancient Grains: A Metabolomics Approach

Directory of Open Access Journals (Sweden)

Laura Righetti

2016-07-01

Full Text Available Hulled, or ancient, wheats were the earliest domesticated wheats by mankind and the ancestors of current wheats. Their cultivation drastically decreased during the 1960s; however, the increasing demand for a healthy and equilibrated diet led to rediscovering these grains. Our aim was to use a non-targeted metabolomic approach to discriminate and characterize similarities and differences between ancient Triticum varieties. For this purpose, 77 hulled wheat samples from three different varieties were collected: Garfagnana T. turgidum var. dicoccum L. (emmer, ID331 T. monococcum L. (einkorn and Rouquin T. spelta L. (spelt. The ultra high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-QTOF metabolomics approach highlighted a pronounced sample clustering according to the wheat variety, with an excellent predictability (Q2, for all the models built. Fifteen metabolites were tentatively identified based on accurate masses, isotopic pattern, and product ion spectra. Among these, alkylresorcinols (ARs were found to be significantly higher in spelt and emmer, showing different homologue composition. Furthermore, phosphatidylcholines (PC and lysophosphatidylcholines (lysoPC levels were higher in einkorn variety. The results obtained in this study confirmed the importance of ARs as markers to distinguish between Triticum species and revealed their values as cultivar markers, being not affected by the environmental influences.

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

Science.gov (United States)

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.

MassCascade: Visual Programming for LC-MS Data Processing in Metabolomics.

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Beisken, Stephan; Earll, Mark; Portwood, David; Seymour, Mark; Steinbeck, Christoph

2014-04-01

Liquid chromatography coupled to mass spectrometry (LC-MS) is commonly applied to investigate the small molecule complement of organisms. Several software tools are typically joined in custom pipelines to semi-automatically process and analyse the resulting data. General workflow environments like the Konstanz Information Miner (KNIME) offer the potential of an all-in-one solution to process LC-MS data by allowing easy integration of different tools and scripts. We describe MassCascade and its workflow plug-in for processing LC-MS data. The Java library integrates frequently used algorithms in a modular fashion, thus enabling it to serve as back-end for graphical front-ends. The functions available in MassCascade have been encapsulated in a plug-in for the workflow environment KNIME, allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming. The design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated. MassCascade is an open-source library for LC-MS data processing in metabolomics. It embraces the concept of visual programming through its KNIME plug-in, simplifying the process of building complex workflows. The library was validated using open data.

Fish mucus metabolome reveals fish life-history traits

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Reverter, M.; Sasal, P.; Banaigs, B.; Lecchini, D.; Lecellier, G.; Tapissier-Bontemps, N.

2017-06-01

Fish mucus has important biological and ecological roles such as defense against fish pathogens and chemical mediation among several species. A non-targeted liquid chromatography-mass spectrometry metabolomic approach was developed to study gill mucus of eight butterflyfish species in Moorea (French Polynesia), and the influence of several fish traits (geographic site and reef habitat, species taxonomy, phylogeny, diet and parasitism levels) on the metabolic variability was investigated. A biphasic extraction yielding two fractions (polar and apolar) was used. Fish diet (obligate corallivorous, facultative corallivorous or omnivorous) arose as the main driver of the metabolic differences in the gill mucus in both fractions, accounting for 23% of the observed metabolic variability in the apolar fraction and 13% in the polar fraction. A partial least squares discriminant analysis allowed us to identify the metabolites (variable important in projection, VIP) driving the differences between fish with different diets (obligate corallivores, facultative corallivores and omnivorous). Using accurate mass data and fragmentation data, we identified some of these VIP as glycerophosphocholines, ceramides and fatty acids. Level of monogenean gill parasites was the second most important factor shaping the gill mucus metabolome, and it explained 10% of the metabolic variability in the polar fraction and 5% in the apolar fraction. A multiple regression tree revealed that the metabolic variability due to parasitism in the polar fraction was mainly due to differences between non-parasitized and parasitized fish. Phylogeny and butterflyfish species were factors contributing significantly to the metabolic variability of the apolar fraction (10 and 3%, respectively) but had a less pronounced effect in the polar fraction. Finally, geographic site and reef habitat of butterflyfish species did not influence the gill mucus metabolome of butterflyfishes.

Using direct infusion mass spectrometry for serum metabolomics in Alzheimer's disease.

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González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

2014-11-01

Currently, there is no cure for Alzheimer's disease and early diagnosis is very difficult, since no biomarkers have been established with the necessary reliability and specificity. For the discovery of new biomarkers, the application of omics is emerging, especially metabolomics based on the use of mass spectrometry. In this work, an analytical approach based on direct infusion electrospray mass spectrometry was applied for the first time to blood serum samples in order to elucidate discriminant metabolites. Complementary methodologies of extraction and mass spectrometry analysis were employed for comprehensive metabolic fingerprinting. Finally, the application of multivariate statistical tools allowed us to discriminate Alzheimer patients and healthy controls, and identify some compounds as potential markers of disease. This approach provided a global vision of disease, given that some important metabolic pathways could be studied, such as membrane destabilization processes, oxidative stress, hypometabolism, or neurotransmission alterations. Most remarkable results are the high levels of phospholipids containing saturated fatty acids, respectively, polyunsaturated ones and the high concentration of whole free fatty acids in Alzheimer's serum samples. Thus, these results represent an interesting approximation to understand the pathogenesis of disease and the identification of potential biomarkers.

New tools and resources in metabolomics: 2016-2017.

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Misra, Biswapriya B

2018-04-01

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metabolomic Studies of Oral Biofilm, Oral Cancer, and Beyond.

Science.gov (United States)

Washio, Jumpei; Takahashi, Nobuhiro

2016-06-02

Oral diseases are known to be closely associated with oral biofilm metabolism, while cancer tissue is reported to possess specific metabolism such as the 'Warburg effect'. Metabolomics might be a useful method for clarifying the whole metabolic systems that operate in oral biofilm and oral cancer, however, technical limitations have hampered such research. Fortunately, metabolomics techniques have developed rapidly in the past decade, which has helped to solve these difficulties. In vivo metabolomic analyses of the oral biofilm have produced various findings. Some of these findings agreed with the in vitro results obtained in conventional metabolic studies using representative oral bacteria, while others differed markedly from them. Metabolomic analyses of oral cancer tissue not only revealed differences between metabolomic profiles of cancer and normal tissue, but have also suggested a specific metabolic system operates in oral cancer tissue. Saliva contains a variety of metabolites, some of which might be associated with oral or systemic disease; therefore, metabolomics analysis of saliva could be useful for identifying disease-specific biomarkers. Metabolomic analyses of the oral biofilm, oral cancer, and saliva could contribute to the development of accurate diagnostic, techniques, safe and effective treatments, and preventive strategies for oral and systemic diseases.

Metabolome analysis - mass spectrometry and microbial primary metabolites

DEFF Research Database (Denmark)

Højer-Pedersen, Jesper Juul

2008-01-01

, and therefore sample preparation is critical for metabolome analysis. The three major steps in sample preparation for metabolite analysis are sampling, extraction and concentration. These three steps were evaluated for the yeast Saccharomyces cerevisiae with primary focus on analysis of a large number...... of metabolites by one method. The results highlighted that there were discrepancies between different methods. To increase the throughput of cultivation, S. cerevisiae was grown in microtitier plates (MTPs), and the growth was found to be comparable with cultivations in shake flasks. The carbon source was either...... a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high-accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI...

A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection

Directory of Open Access Journals (Sweden)

Wang Q

2016-03-01

Full Text Available Qingjun Wang,1,2,* Tao Sun,3,* Yunfeng Cao,1,2,4,5 Peng Gao,2,4,6 Jun Dong,2,4 Yanhua Fang,2 Zhongze Fang,2 Xiaoyu Sun,2 Zhitu Zhu1,2 1Oncology Department 2, The First Affiliated Hospital of Liaoning Medical University, 2Personalized Treatment and Diagnosis Research Center, The First Affiliated Hospital of Liaoning Medical University and Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Jinzhou, 3Department of Internal Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Insititute, Shenyang, 4CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 5Key Laboratory of Contraceptives and Devices Research (NPFPC, Shanghai Engineer and Technology Research Center of Reproductive Health Drug and Devices, Shanghai Institute of Planned Parenthood Research, Shanghai, 6Clinical Laboratory, Dalian Sixth People’s Hospital, Dalian, People’s Republic of China *These authors contributed equally to this work Objective: Breast cancer (BC is still a lethal threat to women worldwide. An accurate screening and diagnosis strategy performed in an easy-to-operate manner is highly warranted in clinical perspective. Besides the routinely focused protein markers, blood is full of small molecular metabolites with diverse structures and properties. This study aimed to screen metabolite markers with BC diagnosis potentials.Methods: A dried blood spot-based direct infusion mass spectrometry (MS metabolomic analysis was conducted for BC and non-BC differentiation. The targeted analytes included 23 amino acids and 26 acylcarnitines.Results: Multivariate analysis screened out 21 BC-related metabolites in the blood. Regression analysis generated a diagnosis model consisting of parameters Pip, Asn, Pro, C14:1/C16, Phe/Tyr, and Gly/Ala. Tested with another set of BC and non-BC samples, this model showed a sensitivity of 92.2% and a specificity

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

NARCIS (Netherlands)

Moco, S.I.A.; Forshed, J.; Vos, de C.H.; Bino, R.J.; Vervoort, J.J.M.

2008-01-01

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were

Metabolomics for Biomarker Discovery: Moving to the Clinic

Science.gov (United States)

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

Impact of Intestinal Microbiota on Intestinal Luminal Metabolome

Science.gov (United States)

Matsumoto, Mitsuharu; Kibe, Ryoko; Ooga, Takushi; Aiba, Yuji; Kurihara, Shin; Sawaki, Emiko; Koga, Yasuhiro; Benno, Yoshimi

2012-01-01

Low–molecular-weight metabolites produced by intestinal microbiota play a direct role in health and disease. In this study, we analyzed the colonic luminal metabolome using capillary electrophoresis mass spectrometry with time-of-flight (CE-TOFMS) —a novel technique for analyzing and differentially displaying metabolic profiles— in order to clarify the metabolite profiles in the intestinal lumen. CE-TOFMS identified 179 metabolites from the colonic luminal metabolome and 48 metabolites were present in significantly higher concentrations and/or incidence in the germ-free (GF) mice than in the Ex-GF mice (p metabolome and a comprehensive understanding of intestinal luminal metabolome is critical for clarifying host-intestinal bacterial interactions. PMID:22724057

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.

Science.gov (United States)

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

DEFF Research Database (Denmark)

Kamstrup-Nielsen, Maja Hermann

Metabolomics is the analysis of the whole metabolome and the focus in metabolomics studies is to measure as many metabolites as possible. The use of chemometrics in metabolomics studies is widespread, but there is a clear lack of validation in the developed models. The focus in this thesis has been...... how to properly handle complex metabolomics data, in order to achieve reliable and valid multivariate models. This has been illustrated by three case studies with examples of forecasting breast cancer and early detection of colorectal cancer based on data from nuclear magnetic resonance (NMR...... is a presentation of a core consistency diagnostic aiding in determining the number of components in a PARAFAC2 model. It is of great importance to validate especially PLS-DA models and if not done properly, the developed models might reveal spurious groupings. Furthermore, data from metabolomics studies contain...

Accurate mass measurements on neutron-deficient krypton isotopes

CERN Document Server

Rodríguez, D.; Äystö, J.; Beck, D.; Blaum, K.; Bollen, G.; Herfurth, F.; Jokinen, A.; Kellerbauer, A.; Kluge, H.-J.; Kolhinen, V.S.; Oinonen, M.; Sauvan, E.; Schwarz, S.

2006-01-01

The masses of $^{72–78,80,82,86}$Kr were measured directly with the ISOLTRAP Penning trap mass spectrometer at ISOLDE/CERN. For all these nuclides, the measurements yielded mass uncertainties below 10 keV. The ISOLTRAP mass values for $^{72–75}$Kr being more precise than the previous results obtained by means of other techniques, and thus completely determine the new values in the Atomic-Mass Evaluation. Besides the interest of these masses for nuclear astrophysics, nuclear structure studies, and Standard Model tests, these results constitute a valuable and accurate input to improve mass models. In this paper, we present the mass measurements and discuss the mass evaluation for these Kr isotopes.

Metabolomic Profiles of Dinophysis acuminata and Dinophysis acuta Using Non-Targeted High-Resolution Mass Spectrometry: Effect of Nutritional Status and Prey

Directory of Open Access Journals (Sweden)

María García-Portela

2018-04-01

Full Text Available Photosynthetic species of the genus Dinophysis are obligate mixotrophs with temporary plastids (kleptoplastids that are acquired from the ciliate Mesodinium rubrum, which feeds on cryptophytes of the Teleaulax-Plagioselmis-Geminigera clade. A metabolomic study of the three-species food chain Dinophysis-Mesodinium-Teleaulax was carried out using mass spectrometric analysis of extracts of batch-cultured cells of each level of that food chain. The main goal was to compare the metabolomic expression of Galician strains of Dinophysis acuminata and D. acuta that were subjected to different feeding regimes (well-fed and prey-limited and feeding on two Mesodinium (Spanish and Danish strains. Both Dinophysis species were able to grow while feeding on both Mesodinium strains, although differences in growth rates were observed. Toxin and metabolomic profiles of the two Dinophysis species were significantly different, and also varied between different feeding regimes and different prey organisms. Furthermore, significantly different metabolomes were expressed by a strain of D. acuminata that was feeding on different strains of the ciliate Mesodinium rubrum. Both species-specific metabolites and those common to D. acuminata and D. acuta were tentatively identified by screening of METLIN and Marine Natural Products Dictionary databases. This first metabolomic study applied to Dinophysis acuminata and D.acuta in culture establishes a basis for the chemical inventory of these species.

Metabolomic Profiles of Dinophysis acuminata and Dinophysis acuta Using Non-Targeted High-Resolution Mass Spectrometry: Effect of Nutritional Status and Prey.

Science.gov (United States)

García-Portela, María; Reguera, Beatriz; Sibat, Manoella; Altenburger, Andreas; Rodríguez, Francisco; Hess, Philipp

2018-04-26

Photosynthetic species of the genus Dinophysis are obligate mixotrophs with temporary plastids (kleptoplastids) that are acquired from the ciliate Mesodinium rubrum , which feeds on cryptophytes of the Teleaulax-Plagioselmis-Geminigera clade. A metabolomic study of the three-species food chain Dinophysis-Mesodinium-Teleaulax was carried out using mass spectrometric analysis of extracts of batch-cultured cells of each level of that food chain. The main goal was to compare the metabolomic expression of Galician strains of Dinophysis acuminata and D. acuta that were subjected to different feeding regimes (well-fed and prey-limited) and feeding on two Mesodinium (Spanish and Danish) strains. Both Dinophysis species were able to grow while feeding on both Mesodinium strains, although differences in growth rates were observed. Toxin and metabolomic profiles of the two Dinophysis species were significantly different, and also varied between different feeding regimes and different prey organisms. Furthermore, significantly different metabolomes were expressed by a strain of D. acuminata that was feeding on different strains of the ciliate Mesodinium rubrum . Both species-specific metabolites and those common to D. acuminata and D. acuta were tentatively identified by screening of METLIN and Marine Natural Products Dictionary databases. This first metabolomic study applied to Dinophysis acuminata and D.acuta in culture establishes a basis for the chemical inventory of these species.

Metabolomic Studies of Oral Biofilm, Oral Cancer, and Beyond

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Jumpei Washio

2016-06-01

Full Text Available Oral diseases are known to be closely associated with oral biofilm metabolism, while cancer tissue is reported to possess specific metabolism such as the ‘Warburg effect’. Metabolomics might be a useful method for clarifying the whole metabolic systems that operate in oral biofilm and oral cancer, however, technical limitations have hampered such research. Fortunately, metabolomics techniques have developed rapidly in the past decade, which has helped to solve these difficulties. In vivo metabolomic analyses of the oral biofilm have produced various findings. Some of these findings agreed with the in vitro results obtained in conventional metabolic studies using representative oral bacteria, while others differed markedly from them. Metabolomic analyses of oral cancer tissue not only revealed differences between metabolomic profiles of cancer and normal tissue, but have also suggested a specific metabolic system operates in oral cancer tissue. Saliva contains a variety of metabolites, some of which might be associated with oral or systemic disease; therefore, metabolomics analysis of saliva could be useful for identifying disease-specific biomarkers. Metabolomic analyses of the oral biofilm, oral cancer, and saliva could contribute to the development of accurate diagnostic, techniques, safe and effective treatments, and preventive strategies for oral and systemic diseases.

Microbial metabolomics in open microscale platforms

Science.gov (United States)

Barkal, Layla J.; Theberge, Ashleigh B.; Guo, Chun-Jun; Spraker, Joe; Rappert, Lucas; Berthier, Jean; Brakke, Kenneth A.; Wang, Clay C. C.; Beebe, David J.; Keller, Nancy P.; Berthier, Erwin

2016-01-01

The microbial secondary metabolome encompasses great synthetic diversity, empowering microbes to tune their chemical responses to changing microenvironments. Traditional metabolomics methods are ill-equipped to probe a wide variety of environments or environmental dynamics. Here we introduce a class of microscale culture platforms to analyse chemical diversity of fungal and bacterial secondary metabolomes. By leveraging stable biphasic interfaces to integrate microculture with small molecule isolation via liquid–liquid extraction, we enable metabolomics-scale analysis using mass spectrometry. This platform facilitates exploration of culture microenvironments (including rare media typically inaccessible using established methods), unusual organic solvents for metabolite isolation and microbial mutants. Utilizing Aspergillus, a fungal genus known for its rich secondary metabolism, we characterize the effects of culture geometry and growth matrix on secondary metabolism, highlighting the potential use of microscale systems to unlock unknown or cryptic secondary metabolites for natural products discovery. Finally, we demonstrate the potential for this class of microfluidic systems to study interkingdom communication between fungi and bacteria. PMID:26842393

Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry.

Science.gov (United States)

Kami, Kenjiro; Fujimori, Tamaki; Sato, Hajime; Sato, Mutsuko; Yamamoto, Hiroyuki; Ohashi, Yoshiaki; Sugiyama, Naoyuki; Ishihama, Yasushi; Onozuka, Hiroko; Ochiai, Atsushi; Esumi, Hiroyasu; Soga, Tomoyoshi; Tomita, Masaru

2013-04-01

Metabolic microenvironment of tumor cells is influenced by oncogenic signaling and tissue-specific metabolic demands, blood supply, and enzyme expression. To elucidate tumor-specific metabolism, we compared the metabolomics of normal and tumor tissues surgically resected pairwise from nine lung and seven prostate cancer patients, using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Phosphorylation levels of enzymes involved in central carbon metabolism were also quantified. Metabolomic profiles of lung and prostate tissues comprised 114 and 86 metabolites, respectively, and the profiles not only well distinguished tumor from normal tissues, but also squamous cell carcinoma from the other tumor types in lung cancer and poorly differentiated tumors from moderately differentiated tumors in prostate cancer. Concentrations of most amino acids, especially branched-chain amino acids, were significantly higher in tumor tissues, independent of organ type, but of essential amino acids were particularly higher in poorly differentiated than moderately differentiated prostate cancers. Organ-dependent differences were prominent at the levels of glycolytic and tricarboxylic acid cycle intermediates and associated energy status. Significantly high lactate concentrations and elevated activating phosphorylation levels of phosphofructokinase and pyruvate kinase in lung tumors confirmed hyperactive glycolysis. We highlighted the potential of CE-TOFMS-based metabolomics combined with phosphorylated enzyme analysis for understanding tissue-specific tumor microenvironments, which may lead to the development of more effective and specific anticancer therapeutics.

Biomarker discovery in neurological diseases: a metabolomic approach

Directory of Open Access Journals (Sweden)

Afaf El-Ansary

2009-12-01

Full Text Available Afaf El-Ansary, Nouf Al-Afaleg, Yousra Al-YafaeeBiochemistry Department, Science College, King Saud University, Riyadh, Saudi ArabiaAbstract: Biomarkers are pharmacological and physiological measurements or specific biochemicals in the body that have a particular molecular feature that makes them useful for measuring the progress of disease or the effects of treatment. Due to the complexity of neurological disorders, it is very difficult to have perfect markers. Brain diseases require plenty of markers to reflect the metabolic impairment of different brain cells. The recent introduction of the metabolomic approach helps the study of neurological diseases based on profiling a multitude of biochemical components related to brain metabolism. This review is a trial to elucidate the possibility to use this approach to identify plasma metabolic markers related to neurological disorders. Previous trials using different metabolomic analyses including nuclear magnetic resonance spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, and capillary electrophoresis will be traced.Keywords: metabolic biomarkers, neurological disorders. metabolome, nuclear magnetic resonance, mass spectrometry, chromatography

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

Energy Technology Data Exchange (ETDEWEB)

Wang, Yang; Liu, Fang; Li, Peng; He, Chengwei; Wang, Ruibing; Su, Huanxing; Wan, Jian-Bo, E-mail: jbwan@umac.mo

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. - Highlights: • An UHPLC/Q-TOF tsMIM MS-based pseudotargeted metabolomics was proposed. • Compared to full scan, the improved method exhibits better repeatability and a wider linear range. • The proposed method could achieve pseudotargeted analysis on one UHPLC/Q-TOF/MS instrument. • The developed method was successfully used to discover biomarkers for alcohol-induced liver injury.

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

International Nuclear Information System (INIS)

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

2016-01-01

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. - Highlights: • An UHPLC/Q-TOF tsMIM MS-based pseudotargeted metabolomics was proposed. • Compared to full scan, the improved method exhibits better repeatability and a wider linear range. • The proposed method could achieve pseudotargeted analysis on one UHPLC/Q-TOF/MS instrument. • The developed method was successfully used to discover biomarkers for alcohol-induced liver injury.

Leg mass characteristics of accurate and inaccurate kickers--an Australian football perspective.

Science.gov (United States)

Hart, Nicolas H; Nimphius, Sophia; Cochrane, Jodie L; Newton, Robert U

2013-01-01

Athletic profiling provides valuable information to sport scientists, assisting in the optimal design of strength and conditioning programmes. Understanding the influence these physical characteristics may have on the generation of kicking accuracy is advantageous. The aim of this study was to profile and compare the lower limb mass characteristics of accurate and inaccurate Australian footballers. Thirty-one players were recruited from the Western Australian Football League to perform ten drop punt kicks over 20 metres to a player target. Players were separated into accurate (n = 15) and inaccurate (n = 16) groups, with leg mass characteristics assessed using whole body dual energy x-ray absorptiometry (DXA) scans. Accurate kickers demonstrated significantly greater relative lean mass (P ≤ 0.004) and significantly lower relative fat mass (P ≤ 0.024) across all segments of the kicking and support limbs, while also exhibiting significantly higher intra-limb lean-to-fat mass ratios for all segments across both limbs (P ≤ 0.009). Inaccurate kickers also produced significantly larger asymmetries between limbs than accurate kickers (P ≤ 0.028), showing considerably lower lean mass in their support leg. These results illustrate a difference in leg mass characteristics between accurate and inaccurate kickers, highlighting the potential influence these may have on technical proficiency of the drop punt.

Global mass spectrometry based metabolomics profiling of erythrocytes infected with Plasmodium falciparum.

Directory of Open Access Journals (Sweden)

Theodore R Sana

Full Text Available Malaria is a global infectious disease that threatens the lives of millions of people. Transcriptomics, proteomics and functional genomics studies, as well as sequencing of the Plasmodium falciparum and Homo sapiens genomes, have shed new light on this host-parasite relationship. Recent advances in accurate mass measurement mass spectrometry, sophisticated data analysis software, and availability of biological pathway databases, have converged to facilitate our global, untargeted biochemical profiling study of in vitro P. falciparum-infected (IRBC and uninfected (NRBC erythrocytes. In order to expand the number of detectable metabolites, several key analytical steps in our workflows were optimized. Untargeted and targeted data mining resulted in detection of over one thousand features or chemical entities. Untargeted features were annotated via matching to the METLIN metabolite database. For targeted data mining, we queried the data using a compound database derived from a metabolic reconstruction of the P. falciparum genome. In total, over one hundred and fifty differential annotated metabolites were observed. To corroborate the representation of known biochemical pathways from our data, an inferential pathway analysis strategy was used to map annotated metabolites onto the BioCyc pathway collection. This hypothesis-generating approach resulted in over-representation of many metabolites onto several IRBC pathways, most prominently glycolysis. In addition, components of the "branched" TCA cycle, partial urea cycle, and nucleotide, amino acid, chorismate, sphingolipid and fatty acid metabolism were found to be altered in IRBCs. Interestingly, we detected and confirmed elevated levels for cyclic ADP ribose and phosphoribosyl AMP in IRBCs, a novel observation. These metabolites may play a role in regulating the release of intracellular Ca(2+ during P. falciparum infection. Our results support a strategy of global metabolite profiling by untargeted

Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics.

Science.gov (United States)

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

MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics.

Science.gov (United States)

Zhou, Zhiwei; Xiong, Xin; Zhu, Zheng-Jiang

2017-07-15

In metabolomics, rigorous structural identification of metabolites presents a challenge for bioinformatics. The use of collision cross-section (CCS) values of metabolites derived from ion mobility-mass spectrometry effectively increases the confidence of metabolite identification, but this technique suffers from the limit number of available CCS values. Currently, there is no software available for rapidly generating the metabolites' CCS values. Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics. The web server is freely available at: http://www.metabolomics-shanghai.org/MetCCS/ . jiangzhu@sioc.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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

Science.gov (United States)

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.

metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics

NARCIS (Netherlands)

Wehrens, H.R.M.J.; Weingart, G.; Mattivi, F.

2014-01-01

Untargeted metabolomics are rapidly becoming an important tool for studying complex biological samples. Gas chromatography–mass spectrometry (GC–MS) is the most widely used analytical technology for metabolomic analysis of compounds that are volatile or can be chemically derivatised into volatile

Metabolomics Analysis of Health Functions of Physalis Pubescens L. using by Ultra-performance Liquid Chromatography/Electrospray Ionization Quadruple Time-of-Flight Mass Spectrometry

Institute of Scientific and Technical Information of China (English)

Hang Chu; Hui Sun; Guang-Li Yan; Ai-Hua Zhang; Chang Liu; Hui Dong; Xiang-Cai Meng; Xi-Jun Wang

2015-01-01

Herbal medicines may benefit from metabolomics studies, and applying metabolomics may provide answers about which herbal interventions may be effective for individuals, which metabolic processes are triggered, and the subsequent chemical pathways of activity. Physalis pubescens L (PPL) is an herbal fruit for one year living plant and has been developed into healthy function’s food. However, the mechanisms of health functions are still unclear. To comprehensively and holistically assess its anti-fatigue and antioxidant effects, a novel integrative metabolomics approach was applied. In this study, we present metabolomics analysis applying ultra performance liquid chromatography coupled to quadrupole with time-of-flight mass spectrometry (UPLC-Q/TOF-MS) to determine metabolite alterations after oral administration PPL to rats. Fifteen metabolites in urine were identified as potential biomarkers. Pattern analysis of the UPLC-Q/TOF-MS data disclosed that PPL could relieve fatigue rats by ameliorating the disturbance in amino acids metabolism and energy metabolism, alleviating the oxidative stress from reactive oxygen species and the inflammatory damage, and recovering the destructed regulation. Based on these results, we demonstrated that PPL is a promising source of natural anti-fatigue and antioxidants material for use in functional foods and medicines.

A robust and versatile mass spectrometry platform for comprehensive assessment of the thiol redox metabolome

Directory of Open Access Journals (Sweden)

T.R. Sutton

2018-06-01

Full Text Available Several diseases are associated with perturbations in redox signaling and aberrant hydrogen sulfide metabolism, and numerous analytical methods exist for the measurement of the sulfur-containing species affected. However, uncertainty remains about their concentrations and speciation in cells/biofluids, perhaps in part due to differences in sample processing and detection principles. Using ultrahigh-performance liquid chromatography in combination with electrospray-ionization tandem mass spectrometry we here outline a specific and sensitive platform for the simultaneous measurement of 12 analytes, including total and free thiols, their disulfides and sulfide in complex biological matrices such as blood, saliva and urine. Total assay run time is < 10 min, enabling high-throughput analysis. Enhanced sensitivity and avoidance of artifactual thiol oxidation is achieved by taking advantage of the rapid reaction of sulfhydryl groups with N-ethylmaleimide. We optimized the analytical procedure for detection and separation conditions, linearity and precision including three stable isotope labelled standards. Its versatility for future more comprehensive coverage of the thiol redox metabolome was demonstrated by implementing additional analytes such as methanethiol, N-acetylcysteine, and coenzyme A. Apparent plasma sulfide concentrations were found to vary substantially with sample pretreatment and nature of the alkylating agent. In addition to protein binding in the form of mixed disulfides (S-thiolation a significant fraction of aminothiols and sulfide appears to be also non-covalently associated with proteins. Methodological accuracy was tested by comparing the plasma redox status of 10 healthy human volunteers to a well-established protocol optimized for reduced/oxidized glutathione. In a proof-of-principle study a deeper analysis of the thiol redox metabolome including free reduced/oxidized as well as bound thiols and sulfide was performed

BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics.

Science.gov (United States)

Avtonomov, Dmitry M; Raskind, Alexander; Nesvizhskii, Alexey I

2016-08-05

Mass spectrometry (MS) coupled to liquid chromatography (LC) is a commonly used technique in metabolomic and proteomic research. As the size and complexity of LC-MS-based experiments grow, it becomes increasingly more difficult to perform quality control of both raw data and processing results. In a practical setting, quality control steps for raw LC-MS data are often overlooked, and assessment of an experiment's success is based on some derived metrics such as "the number of identified compounds". The human brain interprets visual data much better than plain text, hence the saying "a picture is worth a thousand words". Here, we present the BatMass software package, which allows for performing quick quality control of raw LC-MS data through its fast visualization capabilities. It also serves as a testbed for developers of LC-MS data processing algorithms by providing a data access library for open mass spectrometry file formats and a means of visually mapping processing results back to the original data. We illustrate the utility of BatMass with several use cases of quality control and data exploration.

Livestock metabolomics and the livestock metabolome: A systematic review

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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.

Science.gov (United States)

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.

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience*

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Griss, Johannes; Jones, Andrew R.; Sachsenberg, Timo; Walzer, Mathias; Gatto, Laurent; Hartler, Jürgen; Thallinger, Gerhard G.; Salek, Reza M.; Steinbeck, Christoph; Neuhauser, Nadin; Cox, Jürgen; Neumann, Steffen; Fan, Jun; Reisinger, Florian; Xu, Qing-Wei; del Toro, Noemi; Pérez-Riverol, Yasset; Ghali, Fawaz; Bandeira, Nuno; Xenarios, Ioannis; Kohlbacher, Oliver; Vizcaíno, Juan Antonio; Hermjakob, Henning

2014-01-01

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online. PMID:24980485

Metabolomics data normalization with EigenMS.

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Yuliya V Karpievitch

Full Text Available Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated. Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05 as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.

Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification.

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Cao, Mingshu; Fraser, Karl; Rasmussen, Susanne

2013-10-31

Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package "iontree" that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.

YMDB: the Yeast Metabolome Database

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Jewison, Timothy; Knox, Craig; Neveu, Vanessa; Djoumbou, Yannick; Guo, An Chi; Lee, Jacqueline; Liu, Philip; Mandal, Rupasri; Krishnamurthy, Ram; Sinelnikov, Igor; Wilson, Michael; Wishart, David S.

2012-01-01

The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated ‘metabolomic’ database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiae's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry. PMID:22064855

Application of Metabolomics in Thyroid Cancer Research

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Anna Wojakowska

2015-01-01

Full Text Available Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput “omics” techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Impacts on the metabolome of down-regulating polyphenol oxidase in transgenic potato tubers

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Tubers of potato (Solanum tuberosum L. cv. Estima) genetically modified (GM) to reduce polyphenol oxidase (PPO) activity and enzymatic discolouration were assessed for changes in the metabolome using Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography (GC)-MS. Metabolome changes ...

Fecal Metabolomics of Type 2 Diabetic Rats and Treatment with Gardenia jasminoides Ellis Based on Mass Spectrometry Technique.

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Zhou, Yuan; Men, Lihui; Pi, Zifeng; Wei, Mengying; Song, Fengrui; Zhao, Chunfang; Liu, Zhiqiang

2018-02-14

Modern studies have indicated Gardenia jasminoides Ellis (G. jasminoides) showed positive effect in treating type 2 diabetes mellitus (T2DM). In this study, 60 streptozotocin-induced T2DM rats were divided into four groups: type 2 diabetes control group, geniposide-treated group, total iridoid glycosides-treated group, and crude extraction of gardenlae fructus-treated group. The other ten healthy rats were the healthy control group. During 12 weeks of treatment, rat's feces samples were collected for the metabolomics study based on mass spectrometry technique. On the basis of the fecal metabolomics method, 19 potential biomarkers were screened and their relative intensities in each group were compared. The results revealed G. jasminoides mainly regulated dysfunctions in phenylalanine metabolism, tryptophan metabolism, and secondary bile acid biosynthesis pathways induced by diabetes. The current study provides new insight for metabonomics methodology toward T2DM, and the results show that feces can preferably reflect the liver and intestines disorders.

Challenges of metabolomics in human gut microbiota research.

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Smirnov, Kirill S; Maier, Tanja V; Walker, Alesia; Heinzmann, Silke S; Forcisi, Sara; Martinez, Inés; Walter, Jens; Schmitt-Kopplin, Philippe

2016-08-01

The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine. Copyright © 2016 Elsevier GmbH. All rights reserved.

Ion trace detection algorithm to extract pure ion chromatograms to improve untargeted peak detection quality for liquid chromatography/time-of-flight mass spectrometry-based metabolomics data.

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Wang, San-Yuan; Kuo, Ching-Hua; Tseng, Yufeng J

2015-03-03

Able to detect known and unknown metabolites, untargeted metabolomics has shown great potential in identifying novel biomarkers. However, elucidating all possible liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) ion signals in a complex biological sample remains challenging since many ions are not the products of metabolites. Methods of reducing ions not related to metabolites or simply directly detecting metabolite related (pure) ions are important. In this work, we describe PITracer, a novel algorithm that accurately detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms and detect chromatographic peaks. PITracer estimates the relative mass difference tolerance of ions and calibrates the mass over charge (m/z) values for peak detection algorithms with an additional option to further mass correction with respect to a user-specified metabolite. PITracer was evaluated using two data sets containing 373 human metabolite standards, including 5 saturated standards considered to be split peaks resultant from huge m/z fluctuation, and 12 urine samples spiked with 50 forensic drugs of varying concentrations. Analysis of these data sets show that PITracer correctly outperformed existing state-of-art algorithm and extracted the pure ion chromatograms of the 5 saturated standards without generating split peaks and detected the forensic drugs with high recall, precision, and F-score and small mass error.

Tissue Multiplatform-Based Metabolomics/Metabonomics for Enhanced Metabolome Coverage.

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Vorkas, Panagiotis A; Abellona U, M R; Li, Jia V

2018-01-01

The use of tissue as a matrix to elucidate disease pathology or explore intervention comes with several advantages. It allows investigation of the target alteration directly at the focal location and facilitates the detection of molecules that could become elusive after secretion into biofluids. However, tissue metabolomics/metabonomics comes with challenges not encountered in biofluid analyses. Furthermore, tissue heterogeneity does not allow for tissue aliquoting. Here we describe a multiplatform, multi-method workflow which enables metabolic profiling analysis of tissue samples, while it can deliver enhanced metabolome coverage. After applying a dual consecutive extraction (organic followed by aqueous), tissue extracts are analyzed by reversed-phase (RP-) and hydrophilic interaction liquid chromatography (HILIC-) ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) spectroscopy. This pipeline incorporates the required quality control features, enhances versatility, allows provisional aliquoting of tissue extracts for future guided analyses, expands the range of metabolites robustly detected, and supports data integration. It has been successfully employed for the analysis of a wide range of tissue types.

The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience.

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Griss, Johannes; Jones, Andrew R; Sachsenberg, Timo; Walzer, Mathias; Gatto, Laurent; Hartler, Jürgen; Thallinger, Gerhard G; Salek, Reza M; Steinbeck, Christoph; Neuhauser, Nadin; Cox, Jürgen; Neumann, Steffen; Fan, Jun; Reisinger, Florian; Xu, Qing-Wei; Del Toro, Noemi; Pérez-Riverol, Yasset; Ghali, Fawaz; Bandeira, Nuno; Xenarios, Ioannis; Kohlbacher, Oliver; Vizcaíno, Juan Antonio; Hermjakob, Henning

2014-10-01

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

MassTRIX: mass translator into pathways.

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Suhre, Karsten; Schmitt-Kopplin, Philippe

2008-07-01

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org.

Metabolomics in transfusion medicine.

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Nemkov, Travis; Hansen, Kirk C; Dumont, Larry J; D'Alessandro, Angelo

2016-04-01

Biochemical investigations on the regulatory mechanisms of red blood cell (RBC) and platelet (PLT) metabolism have fostered a century of advances in the field of transfusion medicine. Owing to these advances, storage of RBCs and PLT concentrates has become a lifesaving practice in clinical and military settings. There, however, remains room for improvement, especially with regard to the introduction of novel storage and/or rejuvenation solutions, alternative cell processing strategies (e.g., pathogen inactivation technologies), and quality testing (e.g., evaluation of novel containers with alternative plasticizers). Recent advancements in mass spectrometry-based metabolomics and systems biology, the bioinformatics integration of omics data, promise to speed up the design and testing of innovative storage strategies developed to improve the quality, safety, and effectiveness of blood products. Here we review the currently available metabolomics technologies and briefly describe the routine workflow for transfusion medicine-relevant studies. The goal is to provide transfusion medicine experts with adequate tools to navigate through the otherwise overwhelming amount of metabolomics data burgeoning in the field during the past few years. Descriptive metabolomics data have represented the first step omics researchers have taken into the field of transfusion medicine. However, to up the ante, clinical and omics experts will need to merge their expertise to investigate correlative and mechanistic relationships among metabolic variables and transfusion-relevant variables, such as 24-hour in vivo recovery for transfused RBCs. Integration with systems biology models will potentially allow for in silico prediction of metabolic phenotypes, thus streamlining the design and testing of alternative storage strategies and/or solutions. © 2015 AABB.

Application of Stable Isotope-Assisted Metabolomics for Cell Metabolism Studies

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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

Metabolomics in the fight against malaria

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Jorge L Salinas

2014-08-01

Full Text Available Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC, a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.

Stable isotope-resolved metabolomics and applications for drug development

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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

Gas chromatography-mass spectrometry based metabolomic approach for optimization and toxicity evaluation of earthworm sub-lethal responses to carbofuran.

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Mohana Krishna Reddy Mudiam

Full Text Available 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.

Metabolomic analysis of three Mollicute species.

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Anna A Vanyushkina

Full Text Available We present a systematic study of three bacterial species that belong to the class Mollicutes, the smallest and simplest bacteria, Spiroplasma melliferum, Mycoplasma gallisepticum, and Acholeplasma laidlawii. To understand the difference in the basic principles of metabolism regulation and adaptation to environmental conditions in the three species, we analyzed the metabolome of these bacteria. Metabolic pathways were reconstructed using the proteogenomic annotation data provided by our lab. The results of metabolome, proteome and genome profiling suggest a fundamental difference in the adaptation of the three closely related Mollicute species to stress conditions. As the transaldolase is not annotated in Mollicutes, we propose variants of the pentose phosphate pathway catalyzed by annotated enzymes for three species. For metabolite detection we employed high performance liquid chromatography coupled with mass spectrometry. We used liquid chromatography method - hydrophilic interaction chromatography with silica column - as it effectively separates highly polar cellular metabolites prior to their detection by mass spectrometer.

Metabolomic Fingerprints of Individual Algal Cells Using the Single-Probe Mass Spectrometry Technique

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Mei Sun

2018-04-01

Full Text Available Traditional approaches for the assessment of physiological responses of microbes in the environment rely on bulk filtration techniques that obscure differences among populations as well as among individual cells. Here, were report on the development on a novel micro-scale sampling device, referred to as the “Single-probe,” which allows direct extraction of metabolites from living, individual phytoplankton cells for mass spectrometry (MS analysis. The Single-probe is composed of dual-bore quartz tubing which is pulled using a laser pipette puller and fused to a silica capillary and a nano-ESI. For this study, we applied Single-probe MS technology to the marine dinoflagellate Scrippsiella trochoidea, assaying cells grown under different illumination levels and under nitrogen (N limiting conditions as a proof of concept for the technology. In both experiments, significant differences in the cellular metabolome of individual cells could readily be identified, though the vast majority of detected metabolites could not be assigned to KEGG pathways. Using the same approach, significant changes in cellular lipid complements were observed, with individual lipids being both up- and down-regulated under light vs. dark conditions. Conversely, lipid content increased across the board under N limitation, consistent with an adjustment of Redfield stoichiometry to reflect higher C:N and C:P ratios. Overall, these data suggest that the Single-probe MS technique has the potential to allow for near in situ metabolomic analysis of individual phytoplankton cells, opening the door to targeted analyses that minimize cell manipulation and sampling artifacts, while preserving metabolic variability at the cellular level.

Blood transcriptomics and metabolomics for personalized medicine.

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Li, Shuzhao; Todor, Andrei; Luo, Ruiyan

2016-01-01

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

Assessment of Metabolome Annotation Quality: A Method for Evaluating the False Discovery Rate of Elemental Composition Searches

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Matsuda, Fumio; Shinbo, Yoko; Oikawa, Akira; Hirai, Masami Yokota; Fiehn, Oliver; Kanaya, Shigehiko; Saito, Kazuki

2009-01-01

Background In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed. Methodology/Principal Findings The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30–50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries. Conclusions/Significance High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR metabolome data. PMID:19847304

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

Science.gov (United States)

Boiteau, Rene M; Hoyt, David W; Nicora, Carrie D; Kinmonth-Schultz, Hannah A; Ward, Joy K; Bingol, Kerem

2018-01-17

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.

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Energy Technology Data Exchange (ETDEWEB)

Bingol, Kerem; Brüschweiler, Rafael

2017-02-01

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

Liquid Chromatography–Mass Spectrometry Based Metabolomics Study of Cloned versus Normal Pigs Fed Either Restricted or Ad Libitum High-Energy Diets

DEFF Research Database (Denmark)

Christensen, Kirstine Lykke; Hedemann, Mette Skou; Jørgensen, Henry

2012-01-01

Genetically identical cloned pigs should in principle eliminate biological variation and provide more pronounced effects when subjected to, e.g., dietary interventions, but little is known about how phenotype and phenotypic variation is affected by cloning. Therefore, an investigation...... of the metabolome of cloned pigs compared to normal control pigs was performed to elucidate the variation and possible differences in the metabolic phenotypes during a dietary intervention. A total of 19 control pigs and 17 cloned pigs were given the same high-energy dense diet either ad libitum or in a restricted...... manner (60% of ad libitum) for 6 months, and plasma was subjected to liquid chromatography–mass spectrometry nontargeted metabolomics and biochemical analyses. Low systemic levels of IGF-1 could indicate altered growth conditions and energy metabolism in cloned pigs. In response to ad libitum feeding...

Women with preterm birth have a distinct cervicovaginal metabolome.

Science.gov (United States)

Ghartey, Jeny; Bastek, Jamie A; Brown, Amy G; Anglim, Laura; Elovitz, Michal A

2015-06-01

Metabolomics has the potential to reveal novel pathways involved in the pathogenesis of preterm birth (PTB). The objective of this study was to investigate whether the cervicovaginal (CV) metabolome was different in asymptomatic women destined to have a PTB compared with term birth. A nested case-control study was performed using CV fluid collected from a larger prospective cohort. The CV fluid was collected between 20-24 weeks (V1) and 24-28 weeks (V2). The metabolome was compared between women with a spontaneous PTB (n = 10) to women who delivered at term (n = 10). Samples were extracted and prepared for analysis using a standard extraction solvent method. Global biochemical profiles were determined using gas chromatography/mass spectrometry and ultra-performance liquid chromatography/tandem mass spectrometry. An ANOVA was used to detect differences in biochemical compounds between the groups. A false discovery rate was estimated to account for multiple comparisons. A total of 313 biochemicals were identified in CV fluid. Eighty-two biochemicals were different in the CV fluid at V1 in those destined to have a PTB compared with term birth, whereas 48 were different at V2. Amino acid, carbohydrate, and peptide metabolites were distinct between women with and without PTB. These data suggest that the CV space is metabolically active during pregnancy. Changes in the CV metabolome may be observed weeks, if not months, prior to any clinical symptoms. Understanding the CV metabolome may hold promise for unraveling the pathogenesis of PTB and may provide novel biomarkers to identify women most at risk. Copyright © 2015 Elsevier Inc. All rights reserved.

Metabolomics reveals mycoplasma contamination interferes with the metabolism of PANC-1 cells.

Science.gov (United States)

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

Energy Technology Data Exchange (ETDEWEB)

Metz, Thomas O.; Page, Jason S.; Baker, Erin Shammel; Tang, Keqi; Ding, Jie; Shen, Yufeng; Smith, Richard D.

2008-03-31

The goal of metabolomics experiments is the detection and quantitation of as many sample components as reasonably possible in order to identify “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 imperative that metabolome sample constituents be efficiently separated prior to ion production, in order to minimize the phenomenon of ionization suppression. Similarly, optimization of the MS inlet can lead to increased measurement sensitivity. This 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.

Haystack, a web-based tool for metabolomics research.

Science.gov (United States)

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

LC-MS-BASED METABOLOMICS OF XENOBIOTIC-INDUCED TOXICITIES

Directory of Open Access Journals (Sweden)

Chi Chen

2013-01-01

Full Text Available Xenobiotic exposure, especially high-dose or repeated exposure of xenobiotics, can elicit detrimental effects on biological systems through diverse mechanisms. Changes in metabolic systems, including formation of reactive metabolites and disruption of endogenous metabolism, are not only the common consequences of toxic xenobiotic exposure, but in many cases are the major causes behind development of xenobiotic-induced toxicities (XIT. Therefore, examining the metabolic events associated with XIT generates mechanistic insights into the initiation and progression of XIT, and provides guidance for prevention and treatment. Traditional bioanalytical platforms that target only a few suspected metabolites are capable of validating the expected outcomes of xenobiotic exposure. However, these approaches lack the capacity to define global changes and to identify unexpected events in the metabolic system. Recent developments in high-throughput metabolomics have dramatically expanded the scope and potential of metabolite analysis. Among all analytical techniques adopted for metabolomics, liquid chromatography-mass spectrometry (LC-MS has been most widely used for metabolomic investigations of XIT due to its versatility and sensitivity in metabolite analysis. In this review, technical platform of LC-MS-based metabolomics, including experimental model, sample preparation, instrumentation, and data analysis, are discussed. Applications of LC-MS-based metabolomics in exploratory and hypothesis-driven investigations of XIT are illustrated by case studies of xenobiotic metabolism and endogenous metabolism associated with xenobiotic exposure.

Metabolomics for Quality and food security

International Nuclear Information System (INIS)

Diretto, Gianfranco

2015-01-01

By the term 'Metabolomics' means the discipline which allows you to determine the set of small molecules (metabolites) produced by an organism in a given time. The metabolomic analysis requires complex technological platforms that allow, in the first place, the separation (chromatography liquid or gaseous) of the different molecules and, subsequently, the identification of the same on the basis of characteristic ratio between their mass and charge (m / z). This study arises by estimates that, between climate change planned for the coming decades, there will also be quick increasing the concentration of Co2 in the atmosphere. In this context, it is essential to predict how these changes weather will impact on product quality plant at the base of our diet. [it

Sparse Mbplsr for Metabolomics Data and Biomarker Discovery

DEFF Research Database (Denmark)

Karaman, İbrahim

2014-01-01

the link between high throughput metabolomics data generated on different analytical platforms, discover important metabolites deriving from the digestion processes in the gut, and automate metabolic pathway discovery from mass spectrometry. PLS (partial least squares) based chemometric methods were...

Leucine-rich diet alters the 1H-NMR based metabolomic profile without changing the Walker-256 tumour mass in rats.

Science.gov (United States)

Viana, Laís Rosa; Canevarolo, Rafael; Luiz, Anna Caroline Perina; Soares, Raquel Frias; Lubaczeuski, Camila; Zeri, Ana Carolina de Mattos; Gomes-Marcondes, Maria Cristina Cintra

2016-10-03

Cachexia is one of the most important causes of cancer-related death. Supplementation with branched-chain amino acids, particularly leucine, has been used to minimise loss of muscle tissue, although few studies have examined the effect of this type of nutritional supplementation on the metabolism of the tumour-bearing host. Therefore, the present study evaluated whether a leucine-rich diet affects metabolomic derangements in serum and tumour tissues in tumour-bearing Walker-256 rats (providing an experimental model of cachexia). After 21 days feeding Wistar female rats a leucine-rich diet, distributed in L-leucine and LW-leucine Walker-256 tumour-bearing groups, we examined the metabolomic profile of serum and tumour tissue samples and compared them with samples from tumour-bearing rats fed a normal protein diet (C - control; W - tumour-bearing groups). We utilised 1 H-NMR as a means to study the serum and tumour metabolomic profile, tumour proliferation and tumour protein synthesis pathway. Among the 58 serum metabolites examined, we found that 12 were altered in the tumour-bearing group, reflecting an increase in activity of some metabolic pathways related to energy production, which diverted many nutrients toward tumour growth. Despite displaying increased tumour cell activity (i.e., higher Ki-67 and mTOR expression), there were no differences in tumour mass associated with changes in 23 metabolites (resulting from valine, leucine and isoleucine synthesis and degradation, and from the synthesis and degradation of ketone bodies) in the leucine-tumour group. This result suggests that the majority of nutrients were used for host maintenance. A leucine rich-diet, largely used to prevent skeletal muscle loss, did not affect Walker 256 tumour growth and led to metabolomic alterations that may partially explain the positive effects of leucine for the whole tumour-bearing host.

Metabolomics for laboratory diagnostics.

Science.gov (United States)

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.

Assessment of two complementary liquid chromatography coupled to high resolution mass spectrometry metabolomics strategies for the screening of anabolic steroid treatment in calves

International Nuclear Information System (INIS)

Dervilly-Pinel, Gaud; Weigel, Stefan; Lommen, Arjen; Chereau, Sylvain; Rambaud, Lauriane; Essers, Martien; Antignac, Jean-Philippe; Nielen, Michel W.F.; Le Bizec, Bruno

2011-01-01

Anabolic steroids are banned in food producing livestock in Europe. 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. New analytical tools able to detect such abuse are today mandatory. In this context, metabolomics may represent new emerging strategies for investigating the global physiological effects associated to a family of substances and therefore, to suspect the administration of steroids. The purpose of the present study was to set up, assess and compare two complementary mass spectrometry-based metabolomic strategies as new tools to screen for steroid abuse in cattle and demonstrate the feasibility of such approaches. The protocols were developed in two European laboratories in charge of residues analysis in the field of food safety. Apart from sample preparation, the global process was different in both laboratories from LC-HRMS fingerprinting to multivariate data analysis through data processing and involved both LC-Orbitrap-XCMS and UPLC-ToF-MS-MetAlign strategies. The reproducibility of both sample preparation and MS measurements were assessed in order to guarantee that any differences in the acquired fingerprints were not caused by analytical variability but reflect metabolome modifications upon steroids administration. The protocols were then applied to urine samples collected on a large group of animals consisting of 12 control calves and 12 calves administrated with a mixture of 17β-estradiol 3-benzoate and 17β-nandrolone laureate esters according to a protocol reflecting likely illegal practices. The modifications in urine profiles as indicators of steroid administration have been evaluated in this context and proved the suitability of the approach for discriminating anabolic

Assessment of two complementary liquid chromatography coupled to high resolution mass spectrometry metabolomics strategies for the screening of anabolic steroid treatment in calves

Energy Technology Data Exchange (ETDEWEB)

Dervilly-Pinel, Gaud, E-mail: laberca@oniris-nantes.fr [ONIRIS, Ecole nationale veterinaire, agroalimentaire et de l' alimentation Nantes-Atlantique, Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Atlanpole - La Chantrerie, BP 40706, Nantes F-44307 (France); Weigel, Stefan; Lommen, Arjen [RIKILT - Institute of Food Safety, Wageningen UR, P.O. Box 230, 6700 AE Wageningen (Netherlands); Chereau, Sylvain; Rambaud, Lauriane [ONIRIS, Ecole nationale veterinaire, agroalimentaire et de l' alimentation Nantes-Atlantique, Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Atlanpole - La Chantrerie, BP 40706, Nantes F-44307 (France); Essers, Martien [RIKILT - Institute of Food Safety, Wageningen UR, P.O. Box 230, 6700 AE Wageningen (Netherlands); Antignac, Jean-Philippe [ONIRIS, Ecole nationale veterinaire, agroalimentaire et de l' alimentation Nantes-Atlantique, Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Atlanpole - La Chantrerie, BP 40706, Nantes F-44307 (France); Nielen, Michel W.F. [RIKILT - Institute of Food Safety, Wageningen UR, P.O. Box 230, 6700 AE Wageningen (Netherlands); Wageningen University, Laboratory of Organic Chemistry, Wageningen (Netherlands); Le Bizec, Bruno [ONIRIS, Ecole nationale veterinaire, agroalimentaire et de l' alimentation Nantes-Atlantique, Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Atlanpole - La Chantrerie, BP 40706, Nantes F-44307 (France)

2011-08-26

Anabolic steroids are banned in food producing livestock in Europe. 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. New analytical tools able to detect such abuse are today mandatory. In this context, metabolomics may represent new emerging strategies for investigating the global physiological effects associated to a family of substances and therefore, to suspect the administration of steroids. The purpose of the present study was to set up, assess and compare two complementary mass spectrometry-based metabolomic strategies as new tools to screen for steroid abuse in cattle and demonstrate the feasibility of such approaches. The protocols were developed in two European laboratories in charge of residues analysis in the field of food safety. Apart from sample preparation, the global process was different in both laboratories from LC-HRMS fingerprinting to multivariate data analysis through data processing and involved both LC-Orbitrap-XCMS and UPLC-ToF-MS-MetAlign strategies. The reproducibility of both sample preparation and MS measurements were assessed in order to guarantee that any differences in the acquired fingerprints were not caused by analytical variability but reflect metabolome modifications upon steroids administration. The protocols were then applied to urine samples collected on a large group of animals consisting of 12 control calves and 12 calves administrated with a mixture of 17{beta}-estradiol 3-benzoate and 17{beta}-nandrolone laureate esters according to a protocol reflecting likely illegal practices. The modifications in urine profiles as indicators of steroid administration have been evaluated in this context and proved the suitability of the approach for

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

Science.gov (United States)

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.

A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops.

Science.gov (United States)

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.

The effects of gliadin on urine metabolome in mice

DEFF Research Database (Denmark)

Roager, Henrik Munch; Zhang, Li; Frandsen, Henrik Lauritz

Gliadin, a proline-rich protein of gluten, is thought to modulate the gut microbiota and affect the intestinal permeability and immune system. However, little is known about the long-term effects of gliadin on the host and microbial metabolism. To study this, we compared the urine metabolome of two...... groups of mice, which were on a high fat diet with and without gliadin, respectively, for 23 weeks. Using liquid chromatography mass-spectrometry (MS) followed by multivariate analyses we were able to show a clear separation of the two groups of mice based on their urine metabolome. Discriminating...... in the gliadin mice. Also, Maillard reaction products and β-oxidized tocopherols were observed in higher levels in the urine of gliadin mice, suggesting increased oxidative stress in the gliadin mice. Indisputably, gliadin affected the urine metabolome. However, the mechanisms behind the observed metabolite...

Accurate mass and velocity functions of dark matter haloes

Science.gov (United States)

Comparat, Johan; Prada, Francisco; Yepes, Gustavo; Klypin, Anatoly

2017-08-01

N-body cosmological simulations are an essential tool to understand the observed distribution of galaxies. We use the MultiDark simulation suite, run with the Planck cosmological parameters, to revisit the mass and velocity functions. At redshift z = 0, the simulations cover four orders of magnitude in halo mass from ˜1011M⊙ with 8783 874 distinct haloes and 532 533 subhaloes. The total volume used is ˜515 Gpc3, more than eight times larger than in previous studies. We measure and model the halo mass function, its covariance matrix w.r.t halo mass and the large-scale halo bias. With the formalism of the excursion-set mass function, we explicit the tight interconnection between the covariance matrix, bias and halo mass function. We obtain a very accurate (function. We also model the subhalo mass function and its relation to the distinct halo mass function. The set of models obtained provides a complete and precise framework for the description of haloes in the concordance Planck cosmology. Finally, we provide precise analytical fits of the Vmax maximum velocity function up to redshift z publicly available in the Skies and Universes data base.

Experimental design and reporting standards for metabolomics studies of mammalian cell lines.

Science.gov (United States)

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.

Metabolomic Analysis in Brain Research: Opportunities & Challenges

Directory of Open Access Journals (Sweden)

Catherine G Vasilopoulou

2016-05-01

Full Text Available Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s. Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.

Accurate isotope ratio mass spectrometry. Some problems and possibilities

International Nuclear Information System (INIS)

Bievre, P. de

1978-01-01

The review includes reference to 190 papers, mainly published during the last 10 years. It covers the following: important factors in accurate isotope ratio measurements (precision and accuracy of isotope ratio measurements -exemplified by determinations of 235 U/ 238 U and of other elements including 239 Pu/ 240 Pu; isotope fractionation -exemplified by curves for Rb, U); applications (atomic weights); the Oklo natural nuclear reactor (discovered by UF 6 mass spectrometry at Pierrelatte); nuclear and other constants; isotope ratio measurements in nuclear geology and isotope cosmology - accurate age determination; isotope ratio measurements on very small samples - archaeometry; isotope dilution; miscellaneous applications; and future prospects. (U.K.)

Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

Science.gov (United States)

Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang

2014-07-01

Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and

Integration of metabolomics and transcriptomics in nanotoxicity studies.

Science.gov (United States)

Shin, Tae Hwan; Lee, Da Yeon; Lee, Hyeon-Seong; Park, Hyung Jin; Jin, Moon Suk; Paik, Man-Jeong; Manavalan, Balachandran; Mo, Jung-Soon; Lee, Gwang

2018-01-01

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics". [BMB Reports 2018; 51(1): 14-20].

Identification of Plasma Metabolomic Profiling for Diagnosis of Esophageal Squamous-Cell Carcinoma Using an UPLC/TOF/MS Platform

Directory of Open Access Journals (Sweden)

Lihong Yin

2013-04-01

Full Text Available Epidemiological studies indicated that esophageal squamous-cell carcinoma (ESCC is still one of the most common causes of cancer incidence in the world. Searching for valuable markers including circulating endogenous metabolites associated with the risk of esophageal cancer, is extremely important A comparative metabolomics study was performed by using ultraperformance liquid chromatography-electrospray ionization-accurate mass time-of-flight mass spectrometry to analyze 53 pairs of plasma samples from ESCC patients and healthy controls recruited in Huaian, China. The result identified a metabolomic profiling of plasma including 25 upregulated metabolites and five downregulated metabolites, for early diagnosis of ESCC. With a database-based verification protocol, 11 molecules were identified, and six upregulated molecules of interest in ESCC were found to belong to phospholipids as follows: phosphatidylserine, phosphatidic acid, phosphatidyl choline, phosphatidylinositol, phosphatidyl ethanolamine, and sphinganine 1-phosphate. Clinical estimation of metabolic biomarkers through hierarchical cluster analysis in plasma samples from 17 ESCC patients and 29 healthy volunteers indicated that the present metabolite profile could distinguish ESCC patients from healthy individuals. The cluster of aberrant expression of these metabolites in ESCC indicates the critical role of phospholipid metabolism in the oncogenesis of ESCC and suggests its potential ability to assess the risk of ESCC development in addition to currently used risk factors.

The Human Serum Metabolome

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Psychogios, Nikolaos; Hau, David D.; Peng, Jun; Guo, An Chi; Mandal, Rupasri; Bouatra, Souhaila; Sinelnikov, Igor; Krishnamurthy, Ramanarayan; Eisner, Roman; Gautam, Bijaya; Young, Nelson; Xia, Jianguo; Knox, Craig; Dong, Edison; Huang, Paul; Hollander, Zsuzsanna; Pedersen, Theresa L.; Smith, Steven R.; Bamforth, Fiona; Greiner, Russ; McManus, Bruce; Newman, John W.; Goodfriend, Theodore; Wishart, David S.

2011-01-01

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca. PMID:21359215

Metabolomics in chemical ecology.

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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.

Growth of Malignant Non-CNS Tumors Alters Brain Metabolome

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Kovalchuk, Anna; Nersisyan, Lilit; Mandal, Rupasri; Wishart, David; Mancini, Maria; Sidransky, David; Kolb, Bryan; Kovalchuk, Olga

2018-01-01

Cancer survivors experience numerous treatment side effects that negatively affect their quality of life. Cognitive side effects are especially insidious, as they affect memory, cognition, and learning. Neurocognitive deficits occur prior to cancer treatment, arising even before cancer diagnosis, and we refer to them as “tumor brain.” Metabolomics is a new area of research that focuses on metabolome profiles and provides important mechanistic insights into various human diseases, including cancer, neurodegenerative diseases, and aging. Many neurological diseases and conditions affect metabolic processes in the brain. However, the tumor brain metabolome has never been analyzed. In our study we used direct flow injection/mass spectrometry (DI-MS) analysis to establish the effects of the growth of lung cancer, pancreatic cancer, and sarcoma on the brain metabolome of TumorGraft™ mice. We found that the growth of malignant non-CNS tumors impacted metabolic processes in the brain, affecting protein biosynthesis, and amino acid and sphingolipid metabolism. The observed metabolic changes were similar to those reported for neurodegenerative diseases and brain aging, and may have potential mechanistic value for future analysis of the tumor brain phenomenon. PMID:29515623

Sample preparation procedures utilized in microbial metabolomics: An overview.

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Patejko, Małgorzata; Jacyna, Julia; Markuszewski, Michał J

2017-02-01

Bacteria are remarkably diverse in terms of their size, structure and biochemical properties. Due to this fact, it is hard to develop a universal method for handling bacteria cultures during metabolomic analysis. The choice of suitable processing methods constitutes a key element in any analysis, because only appropriate selection of procedures may provide accurate results, leading to reliable conclusions. Because of that, every analytical experiment concerning bacteria requires individually and very carefully planned research methodology. Although every study varies in terms of sample preparation, there are few general steps to follow while planning experiment, like sampling, separation of cells from growth medium, stopping their metabolism and extraction. As a result of extraction, all intracellular metabolites should be washed out from cell environment. What is more, extraction method utilized cannot cause any chemical decomposition or degradation of the metabolome. Furthermore, chosen extraction method should correlate with analytical technique, so it will not disturb or prolong following sample preparation steps. For those reasons, we observe a need to summarize sample preparation procedures currently utilized in microbial metabolomic studies. In the presented overview, papers concerning analysis of extra- and intracellular metabolites, published over the last decade, have been discussed. Presented work gives some basic guidelines that might be useful while planning experiments in microbial metabolomics. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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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

NMR-based milk metabolomics

DEFF Research Database (Denmark)

Sundekilde, Ulrik; Larsen, Lotte Bach; Bertram, Hanne Christine S.

2013-01-01

and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking...... compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining...

Infrared biospectroscopy for a fast qualitative evaluation of sample preparation in metabolomics.

Science.gov (United States)

Kuligowski, Julia; Pérez-Guaita, David; Escobar, Javier; Lliso, Isabel; de la Guardia, Miguel; Lendl, Bernhard; Vento, Máximo; Quintás, Guillermo

2014-09-01

Liquid chromatography-mass spectrometry (LC-MS) has been increasingly used in biomedicine to study the dynamic metabolomic responses of biological systems under different physiological or pathological conditions. To obtain an integrated snapshot of the system, metabolomic methods in biomedicine typically analyze biofluids (e.g. plasma) that require clean-up before being injected into LC-MS systems. However, high resolution LC-MS is costly in terms of resources required for sample and data analysis and care must be taken to prevent chemical (e.g. ion suppression) or statistical artifacts. Because of that, the effect of sample preparation on the metabolomic profile during metabolomic method development is often overlooked. This work combines an Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) and a multivariate exploratory data analysis for a cost-effective qualitative evaluation of major changes in sample composition during sample preparation. ATR-FTIR and LC-time of flight mass spectrometry (TOFMS) data from the analysis of a set of plasma samples precipitated using acetonitrile, methanol and acetone performed in parallel were used as a model example. Biochemical information obtained from the analysis of the ATR-FTIR and LC-TOFMS data was thoroughly compared to evaluate the strengths and shortcomings of FTIR biospectroscopy for assessing sample preparation in metabolomics studies. Results obtained show the feasibility of ATR-FTIR for the evaluation of major trends in the plasma composition changes among different sample pretreatments, providing information in terms of e.g., amino acids, proteins, lipids and carbohydrates overall contents comparable to those found by LC-TOFMS. Copyright © 2014 Elsevier B.V. All rights reserved.

Urinary Metabolomic Study of Chlorogenic Acid in a Rat Model of Chronic Sleep Deprivation Using Gas Chromatography-Mass Spectrometry

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Wei-ni Ma

2018-01-01

Full Text Available The urinary metabolomic study based on gas chromatography-mass spectrometry (GC-MS had been developed to investigate the possible antidepressant mechanism of chlorogenic acid (CGA in a rat model of sleep deprivation (SD. According to pattern recognition analysis, there was a clear separation among big platform group (BP, sleep deprivation group (SD, and the CGA (model + CGA, and CGA group was much closer to the BP group by showing a tendency of recovering towards BP group. Thirty-six significantly changed metabolites related to antidepressant by CGA were identified and used to explore the potential mechanism. Combined with the result of the classic behavioral tests and biochemical indices, CGA has significant antidepressant effects in a rat model of SD, suggesting that the mechanism of action of CGA might be involved in regulating the abnormal pathway of nicotinate and nicotinamide metabolism; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and arginine and proline metabolism. Our results also show that metabolomics analysis based on GC-MS is a useful tool for exploring biomarkers involved in depression and elucidating the potential therapeutic mechanisms of Chinese medicine.

Metabolomics and Its Application in the Development of Discovering Biomarkers for Osteoporosis Research

Directory of Open Access Journals (Sweden)

Huanhuan Lv

2016-12-01

Full Text Available Osteoporosis is a progressive skeletal disorder characterized by low bone mass and increased risk of fracture in later life. The incidence and costs associated with treating osteoporosis cause heavy socio-economic burden. Currently, the diagnosis of osteoporosis mainly depends on bone mineral density and bone turnover markers. However, these indexes are not sensitive and accurate enough to reflect the osteoporosis progression. Metabolomics offers the potential for a holistic approach for clinical diagnoses and treatment, as well as understanding of the pathological mechanism of osteoporosis. In this review, we firstly describe the study subjects of osteoporosis and bio-sample preparation procedures for different analytic purposes, followed by illustrating the biomarkers with potentially predictive, diagnosis and pharmaceutical values when applied in osteoporosis research. Then, we summarize the published metabolic pathways related to osteoporosis. Furthermore, we discuss the importance of chronological data and combination of multi-omics in fully understanding osteoporosis. The application of metabolomics in osteoporosis could provide researchers the opportunity to gain new insight into the metabolic profiling and pathophysiological mechanisms. However, there is still much to be done to validate the potential biomarkers responsible for the progression of osteoporosis and there are still many details needed to be further elucidated.

Metabolomics and Epidemiology Working Group

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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.

Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality.

Science.gov (United States)

Habchi, Baninia; Alves, Sandra; Jouan-Rimbaud Bouveresse, Delphine; Appenzeller, Brice; Paris, Alain; Rutledge, Douglas N; Rathahao-Paris, Estelle

2018-01-01

Due to the presence of pollutants in the environment and food, the assessment of human exposure is required. This necessitates high-throughput approaches enabling large-scale analysis and, as a consequence, the use of high-performance analytical instruments to obtain highly informative metabolomic profiles. In this study, direct introduction mass spectrometry (DIMS) was performed using a Fourier transform ion cyclotron resonance (FT-ICR) instrument equipped with a dynamically harmonized cell. Data quality was evaluated based on mass resolving power (RP), mass measurement accuracy, and ion intensity drifts from the repeated injections of quality control sample (QC) along the analytical process. The large DIMS data size entails the use of bioinformatic tools for the automatic selection of common ions found in all QC injections and for robustness assessment and correction of eventual technical drifts. RP values greater than 10 6 and mass measurement accuracy of lower than 1 ppm were obtained using broadband mode resulting in the detection of isotopic fine structure. Hence, a very accurate relative isotopic mass defect (RΔm) value was calculated. This reduces significantly the number of elemental composition (EC) candidates and greatly improves compound annotation. A very satisfactory estimate of repeatability of both peak intensity and mass measurement was demonstrated. Although, a non negligible ion intensity drift was observed for negative ion mode data, a normalization procedure was easily applied to correct this phenomenon. This study illustrates the performance and robustness of the dynamically harmonized FT-ICR cell to perform large-scale high-throughput metabolomic analyses in routine conditions. Graphical abstract Analytical performance of FT-ICR instrument equipped with a dynamically harmonized cell.

A new approach for accurate mass assignment on a multi-turn time-of-flight mass spectrometer.

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Hondo, Toshinobu; Jensen, Kirk R; Aoki, Jun; Toyoda, Michisato

2017-12-01

A simple, effective accurate mass assignment procedure for a time-of-flight mass spectrometer is desirable. External mass calibration using a mass calibration standard together with an internal mass reference (lock mass) is a common technique for mass assignment, however, using polynomial fitting can result in mass-dependent errors. By using the multi-turn time-of-flight mass spectrometer infiTOF-UHV, we were able to obtain multiple time-of-flight data from an ion monitored under several different numbers of laps that was then used to calculate a mass calibration equation. We have developed a data acquisition system that simultaneously monitors spectra at several different lap conditions with on-the-fly centroid determination and scan law estimation, which is a function of acceleration voltage, flight path, and instrumental time delay. Less than 0.9 mDa mass errors were observed for assigned mass to charge ratios ( m/z) ranging between 4 and 134 using only 40 Ar + as a reference. It was also observed that estimating the scan law on-the-fly provides excellent mass drift compensation.

Structure elucidation of metabolite x17299 by interpretation of mass spectrometric data.

Science.gov (United States)

Zhang, Qibo; Ford, Lisa A; Evans, Anne M; Toal, Douglas R

2017-01-01

A major bottleneck in metabolomic studies is metabolite identification from accurate mass spectrometric data. Metabolite x17299 was identified in plasma as an unknown in a metabolomic study using a compound-centric approach where the associated ion features of the compound were used to determine the true molecular mass. The aim of this work is to elucidate the chemical structure of x17299, a new compound by de novo interpretation of mass spectrometric data. An Orbitrap Elite mass spectrometer was used for acquisition of mass spectra up to MS 4 at high resolution. Synthetic standards of N,N,N -trimethyl-l-alanyl-l-proline betaine (l,l-TMAP), a diastereomer, and an enantiomer were chemically prepared. The planar structure of x17299 was successfully proposed by de novo mechanistic interpretation of mass spectrometric data without any laborious purification and nuclear magnetic resonance spectroscopic analysis. The proposed structure was verified by deuterium exchanged mass spectrometric analysis and confirmed by comparison to a synthetic standard. Relative configuration of x17299 was determined by direct chromatographic comparison to a pair of synthetic diastereomers. Absolute configuration was assigned after derivatization of x17299 with a chiral auxiliary group followed by its chromatographic comparison to a pair of synthetic standards. The chemical structure of metabolite x17299 was determined to be l,l-TMAP.

Salivary microbiota and metabolome associated with celiac disease.

Science.gov (United States)

Francavilla, Ruggiero; Ercolini, Danilo; Piccolo, Maria; Vannini, Lucia; Siragusa, Sonya; De Filippis, Francesca; De Pasquale, Ilaria; Di Cagno, Raffaella; Di Toma, Michele; Gozzi, Giorgia; Serrazanetti, Diana I; De Angelis, Maria; Gobbetti, Marco

2014-06-01

This study aimed to investigate the salivary microbiota and metabolome of 13 children with celiac disease (CD) under a gluten-free diet (treated celiac disease [T-CD]). The same number of healthy children (HC) was used as controls. The salivary microbiota was analyzed by an integrated approach using culture-dependent and -independent methods. Metabolome analysis was carried out by gas chromatography-mass spectrometry-solid-phase microextraction. Compared to HC, the number of some cultivable bacterial groups (e.g., total anaerobes) significantly (P endodontalis, and Prevotella nanceiensis), together with the smallest amount of Actinobacteria. T-CD children were also characterized by decreased levels of some Actinomyces species, Atopobium species, and Corynebacterium durum. Rothia mucilaginosa was the only Actinobacteria species found at the highest level in T-CD children. As shown by multivariate statistical analyses, the levels of organic volatile compounds markedly differentiated T-CD children. Some compounds (e.g., ethyl-acetate, nonanal, and 2-hexanone) were found to be associated with T-CD children. Correlations (false discovery rate [FDR], oral dysbiosis that could affect the oral metabolome.

Lipidome and metabolome analysis of fresh tobacco leaves in different geographical regions using liquid chromatography-mass spectrometry.

Science.gov (United States)

Li, Lili; Lu, Xin; Zhao, Jieyu; Zhang, Junjie; Zhao, Yanni; Zhao, Chunxia; Xu, Guowang

2015-07-01

The combination of the lipidome and the metabolome can provide much more information in plant metabolomics studies. A method for the simultaneous extraction of the lipidome and the metabolome of fresh tobacco leaves was developed. Method validation was performed on the basis of the optimal ratio of methanol to methyl tert-butyl ether to water (37:45:68) from the design of experiments. Good repeatability was obtained. We found that 92.2% and 91.6% of the peaks for the lipidome and the metabolome were within a relative standard deviation of 20%, accounting for 94.6% and 94.6% of the total abundance, respectively. The intraday and interday precisions were also satisfactory. A total of 230 metabolites, including 129 lipids, were identified. Significant differences were found in lipidomic and metabolomic profiles of fresh tobacco leaves in different geographical regions. Highly unsaturated galactolipids, phosphatidylethanolamines, predominant phosphatidylcholines, most of the polyphenols, amino acids, and polyamines had a higher content in Yunnan province, and low-unsaturation-degree galactolipids, triacylglycerols, glucosylceramides with trihydroxy long-chain bases, acylated sterol glucosides, and some organic acids were more abundant in Henan province. Correlation analysis between differential metabolites and climatic factors indicated the vital importance of temperature. The fatty acid unsaturation degree of galactolipids could be influenced by temperature. Accumulation of polyphenols and decreases in the ratios of stigmasterols to sitosterols and glucosylstigmasterols to glucosylsitosterols were also correlated with lower temperature in Yunnan province. Furthermore, lipids were more sensitive to climatic variations than other metabolites.

Development of high-performance chemical isotope labeling LC-MS for profiling the human fecal metabolome.

Science.gov (United States)

Xu, Wei; Chen, Deying; Wang, Nan; Zhang, Ting; Zhou, Ruokun; Huan, Tao; Lu, Yingfeng; Su, Xiaoling; Xie, Qing; Li, Liang; Li, Lanjuan

2015-01-20

Human fecal samples contain endogenous human metabolites, gut microbiota metabolites, and other compounds. Profiling the fecal metabolome can produce metabolic information that may be used not only for disease biomarker discovery, but also for providing an insight about the relationship of the gut microbiome and human health. In this work, we report a chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method for comprehensive and quantitative analysis of the amine- and phenol-containing metabolites in fecal samples. Differential (13)C2/(12)C2-dansyl labeling of the amines and phenols was used to improve LC separation efficiency and MS detection sensitivity. Water, methanol, and acetonitrile were examined as an extraction solvent, and a sequential water-acetonitrile extraction method was found to be optimal. A step-gradient LC-UV setup and a fast LC-MS method were evaluated for measuring the total concentration of dansyl labeled metabolites that could be used for normalizing the sample amounts of individual samples for quantitative metabolomics. Knowing the total concentration was also useful for optimizing the sample injection amount into LC-MS to maximize the number of metabolites detectable while avoiding sample overloading. For the first time, dansylation isotope labeling LC-MS was performed in a simple time-of-flight mass spectrometer, instead of high-end equipment, demonstrating the feasibility of using a low-cost instrument for chemical isotope labeling metabolomics. The developed method was applied for profiling the amine/phenol submetabolome of fecal samples collected from three families. An average of 1785 peak pairs or putative metabolites were found from a 30 min LC-MS run. From 243 LC-MS runs of all the fecal samples, a total of 6200 peak pairs were detected. Among them, 67 could be positively identified based on the mass and retention time match to a dansyl standard library, while 581 and 3197 peak pairs could be putatively

The food metabolome

DEFF Research Database (Denmark)

Scalbert, Augustin; Brennan, Lorraine; Manach, Claudine

2014-01-01

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......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...... 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...

Accurate EPR radiosensitivity calibration using small sample masses

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Hayes, R. B.; Haskell, E. H.; Barrus, J. K.; Kenner, G. H.; Romanyukha, A. A.

2000-03-01

We demonstrate a procedure in retrospective EPR dosimetry which allows for virtually nondestructive sample evaluation in terms of sample irradiations. For this procedure to work, it is shown that corrections must be made for cavity response characteristics when using variable mass samples. Likewise, methods are employed to correct for empty tube signals, sample anisotropy and frequency drift while considering the effects of dose distribution optimization. A demonstration of the method's utility is given by comparing sample portions evaluated using both the described methodology and standard full sample additive dose techniques. The samples used in this study are tooth enamel from teeth removed during routine dental care. We show that by making all the recommended corrections, very small masses can be both accurately measured and correlated with measurements of other samples. Some issues relating to dose distribution optimization are also addressed.

Accurate EPR radiosensitivity calibration using small sample masses

International Nuclear Information System (INIS)

Hayes, R.B.; Haskell, E.H.; Barrus, J.K.; Kenner, G.H.; Romanyukha, A.A.

2000-01-01

We demonstrate a procedure in retrospective EPR dosimetry which allows for virtually nondestructive sample evaluation in terms of sample irradiations. For this procedure to work, it is shown that corrections must be made for cavity response characteristics when using variable mass samples. Likewise, methods are employed to correct for empty tube signals, sample anisotropy and frequency drift while considering the effects of dose distribution optimization. A demonstration of the method's utility is given by comparing sample portions evaluated using both the described methodology and standard full sample additive dose techniques. The samples used in this study are tooth enamel from teeth removed during routine dental care. We show that by making all the recommended corrections, very small masses can be both accurately measured and correlated with measurements of other samples. Some issues relating to dose distribution optimization are also addressed

Proteomics and Metabolomics: two emerging areas for legume improvement

Directory of Open Access Journals (Sweden)

Abirami eRamalingam

2015-12-01

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 ‘omic’ approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

Mono-colonization with Lactobacillus acidophilus NCFM affects the intestinal metabolome as compared to germ-free mice

DEFF Research Database (Denmark)

Roager, Henrik Munch; Sulek, Karolina; Skov, Kasper

Every single species of the gut microbiota produce low-molecular-weight compounds that are absorbed constantly from the intestinal lumen and carried to systemic circulation where they play a direct role in health and disease. However, very few studies address the host metabolome as a function...... of colonizing bacteria. In this study the effect of the Lactobacillus acidophilus NCFM strain was investigated by comparing the metabolome of mono-colonized and germ-free mice in several compartments. By liquid-chromatography coupled to mass spectrometry, we were able to show that the metabolome differed...

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer

International Nuclear Information System (INIS)

Gu Haiwei; Pan Zhengzheng; Xi Bowei; Asiago, Vincent; Musselman, Brian; Raftery, Daniel

2011-01-01

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, 1 H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer.

Science.gov (United States)

Gu, Haiwei; Pan, Zhengzheng; Xi, Bowei; Asiago, Vincent; Musselman, Brian; Raftery, Daniel

2011-02-07

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology. Copyright © 2010 Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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.

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data

Science.gov (United States)

Bean, Heather D.; Hill, Jane E.; Dimandja, Jean-Marie D.

2015-01-01

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly- resolved peaks, especially those at the extremes of the detector linear range, and no influence on well- chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. PMID:25857541

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.

Science.gov (United States)

Bean, Heather D; Hill, Jane E; Dimandja, Jean-Marie D

2015-05-15

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. Copyright © 2015 Elsevier B.V. All rights reserved.

Nutritional Metabolomics

DEFF Research Database (Denmark)

Gürdeniz, Gözde

strategy influences the patterns identified as important for the nutritional question under study. Therefore, in depth understanding of the study design and the specific effects of the analytical technology on the produced data is extremely important to achieve high quality data handling. Besides data......Metabolomics provides a holistic approach to investigate the perturbations in human metabolism with respect to a specific exposure. In nutritional metabolomics, the research question is generally related to the effect of a specific food intake on metabolic profiles commonly of plasma or urine....... Application of multiple analytical strategies may provide comprehensive information to reach a valid answer to these research questions. In this thesis, I investigated several analytical technologies and data handling strategies in order to evaluate their effects on the biological answer. In metabolomics, one...

Metabolomic Studies in Drosophila.

Science.gov (United States)

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.

The future of metabolomics in ELIXIR

Science.gov (United States)

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.

Science.gov (United States)

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.

Web Server for Peak Detection, Baseline Correction, and Alignment in Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics Data.

Science.gov (United States)

Tian, Tze-Feng; Wang, San-Yuan; Kuo, Tien-Chueh; Tan, Cheng-En; Chen, Guan-Yuan; Kuo, Ching-Hua; Chen, Chi-Hsin Sally; Chan, Chang-Chuan; Lin, Olivia A; Tseng, Y Jane

2016-11-01

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC 2 MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC 2 MS consists of blob/peak detection, baseline correction, and blob alignment. GC 2 MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC 2 MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC 2 MS also directly corrects the raw data baseline. The analytical performance of GC 2 MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC 2 MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC 2 MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC 2 MS is freely accessible at http://gc2ms.web.cmdm.tw .

Metabolomic Elucidation of the Effects of Curcumin on Fibroblast-Like Synoviocytes in Rheumatoid Arthritis

OpenAIRE

Ahn, Joong Kyong; Kim, Sooah; Hwang, Jiwon; Kim, Jungyeon; Lee, You Sun; Koh, Eun-Mi; Kim, Kyoung Heon; Cha, Hoon-Suk

2015-01-01

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease characterized by synovial inflammation and joint disability. Curcumin is known to be effective in ameliorating joint inflammation in RA. To obtain new insights into the effect of curcumin on primary fibroblast-like synoviocytes (FLS, N = 3), which are key effector cells in RA, we employed gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS)-based metabolomics. Metabolomic profiling of tumor necrosis factor (TNF)-α...

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

Science.gov (United States)

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.

Mass spectrometry data of metabolomics analysis of Nepenthes pitchers

Directory of Open Access Journals (Sweden)

Muhammad Aqil Fitri Rosli

2017-10-01

Full Text Available Hybridisation plays a significant role in the evolution and diversification of plants. Hybridisation among Nepenthes species is extensive, either naturally or man-made. To investigate the effects of hybridisation on the chemical compositions, we carried out metabolomics study on pitcher tissue of Nepenthes ampullaria, Nepenthes rafflesiana and their hybrid, Nepenthes × hookeriana. Pitcher samples were harvested and extracted in methanol:chloroform:water via sonication-assisted extraction before analysed using LC-TOF-MS. MS data were analysed using XCMS online version 2.2.5. This is the first MS data report towards the profiling, identification and comprehensive comparison of metabolites present in Nepenthes species.

Toxicity and Detoxification Effects of Herbal Caowu via Ultra Performance Liquid Chromatography/Mass Spectrometry Metabolomics Analyzed using Pattern Recognition Method

Science.gov (United States)

Yan, Yan; Zhang, Aihua; Dong, Hui; Yan, Guangli; Sun, Hui; Wu, Xiuhong; Han, Ying; Wang, Xijun

2017-01-01

Background: Caowu (Radix Aconiti kusnezoffii, CW), the root of Aconitum kusnezoffii Reichb., has widely used clinically in rheumatic arthritis, painful joints, and tumors for thousands of years. However, the toxicity of heart and central nervous system induced by CW still limited the application. Materials and Methods: Metabolomics was performed to identify the sensitive and reliable biomarkers and to characterize the phenotypically biochemical perturbations and potential mechanisms of CW-induced toxicity, and the detoxification by combinatorial intervention of CW with Gancao (Radix Glycyrrhizae) (CG), Baishao (Radix Paeoniae Alba) (CB), and Renshen (Radix Ginseng) (CR) was also analyzed by pattern recognition methods. Results: As a result, the metabolites were characterized and responsible for pentose and glucuronate interconversions, tryptophan metabolism, amino sugar and nucleotide sugar metabolism, taurine and hypotaurine metabolism, fructose and mannose metabolism, and starch and sucrose metabolism, six networks of which were the same to the metabolic pathways of Chuanwu (Radix Aconiti, CHW) group. The ascorbate and aldarate metabolism was also characterized by CW group. The urinary metabolomics also revealed CW-induced serious toxicity to heart and liver. Thirteen significant metabolites were identified and had validated as phenotypic toxicity biomarkers of CW, five biomarkers of which were commonly owned in Aconitum. The changes of toxicity metabolites obtained from combinatorial intervention of CG, CB, and CR also were analyzed to investigate the regulation degree of toxicity biomarkers adjusted by different combinatorial interventions at 6th month. Conclusion: Metabolomics analyses coupled with pattern recognition methods in the evaluation of drug toxicity and finding detoxification methods were highlighted in this work. SUMMARY Metabolomics was performed to characterize the biochemical potential mechanisms of Caowu toxicityThirteen significant metabolites

NMR-based metabolomics applications

DEFF Research Database (Denmark)

Iaccarino, Nunzia

Metabolomics is the scientific discipline that identifies and quantifies endogenous and exogenous metabolites in different biological samples. Metabolites are crucial components of a biological system and they are highly informative about its functional state, due to their closeness to the organism...... focused on the analysis of various samples covering a wide range of fields, namely, food and nutraceutical sciences, cell metabolomics and medicine using a metabolomics approach. Indeed, the first part of the thesis describes two exploratory studies performed on Algerian extra virgin olive oil and apple...... juice from ancient Danish apple cultivars. Both studies revealed variety-related peculiarities that would have been difficult to detect by means of traditional analysis. The second part of the project includes four metabolomics studies performed on samples of biological origin. In particular, the first...

Role of metabolomics in TBI research

Science.gov (United States)

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

ECMDB: The E. coli Metabolome Database

OpenAIRE

Guo, An Chi; Jewison, Timothy; Wilson, Michael; Liu, Yifeng; Knox, Craig; Djoumbou, Yannick; Lo, Patrick; Mandal, Rupasri; Krishnamurthy, Ram; Wishart, David S.

2012-01-01

The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to ?1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. E...

Optimal preprocessing of serum and urine metabolomic data fusion for staging prostate cancer through design of experiment

International Nuclear Information System (INIS)

Zheng, Hong; Cai, Aimin; Zhou, Qi; Xu, Pengtao; Zhao, Liangcai; Li, Chen; Dong, Baijun; Gao, Hongchang

2017-01-01

Accurate classification of cancer stages will achieve precision treatment for cancer. Metabolomics presents biological phenotypes at the metabolite level and holds a great potential for cancer classification. Since metabolomic data can be obtained from different samples or analytical techniques, data fusion has been applied to improve classification accuracy. Data preprocessing is an essential step during metabolomic data analysis. Therefore, we developed an innovative optimization method to select a proper data preprocessing strategy for metabolomic data fusion using a design of experiment approach for improving the classification of prostate cancer (PCa) stages. In this study, urine and serum samples were collected from participants at five phases of PCa and analyzed using a 1 H NMR-based metabolomic approach. Partial least squares-discriminant analysis (PLS-DA) was used as a classification model and its performance was assessed by goodness of fit (R 2 ) and predictive ability (Q 2 ). Results show that data preprocessing significantly affect classification performance and depends on data properties. Using the fused metabolomic data from urine and serum, PLS-DA model with the optimal data preprocessing (R 2  = 0.729, Q 2  = 0.504, P < 0.0001) can effectively improve model performance and achieve a better classification result for PCa stages as compared with that without data preprocessing (R 2  = 0.139, Q 2  = 0.006, P = 0.450). Therefore, we propose that metabolomic data fusion integrated with an optimal data preprocessing strategy can significantly improve the classification of cancer stages for precision treatment. - Highlights: • NMR metabolomic analysis of body fluids can be used for staging prostate cancer. • Data preprocessing is an essential step for metabolomic analysis. • Data fusion improves information recovery for cancer classification. • Design of experiment achieves optimal preprocessing of metabolomic data fusion.

Effects of sample injection amount and time-of-flight mass spectrometric detection dynamic range on metabolome analysis by high-performance chemical isotope labeling LC-MS.

Science.gov (United States)

Zhou, Ruokun; Li, Liang

2015-04-06

The effect of sample injection amount on metabolome analysis in a chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) platform was investigated. The performance of time-of-flight (TOF) mass spectrometers with and without a high-dynamic-range (HD) detection system was compared in the analysis of (12)C2/(13)C2-dansyl labeled human urine samples. An average of 1635 ± 21 (n = 3) peak pairs or putative metabolites was detected using the HD-TOF-MS, compared to 1429 ± 37 peak pairs from a conventional or non-HD TOF-MS. In both instruments, signal saturation was observed. However, in the HD-TOF-MS, signal saturation was mainly caused by the ionization process, while in the non-HD TOF-MS, it was caused by the detection process. To extend the MS detection range in the non-HD TOF-MS, an automated switching from using (12)C to (13)C-natural abundance peaks for peak ratio calculation when the (12)C peaks are saturated has been implemented in IsoMS, a software tool for processing CIL LC-MS data. This work illustrates that injecting an optimal sample amount is important to maximize the metabolome coverage while avoiding the sample carryover problem often associated with over-injection. A TOF mass spectrometer with an enhanced detection dynamic range can also significantly increase the number of peak pairs detected. In chemical isotope labeling (CIL) LC-MS, relative metabolite quantification is done by measuring the peak ratio of a (13)C2-/(12)C2-labeled peak pair for a given metabolite present in two comparative samples. The dynamic range of peak ratio measurement does not need to be very large, as only subtle changes of metabolite concentrations are encountered in most metabolomic studies where relative metabolome quantification of different groups of samples is performed. However, the absolute concentrations of different metabolites can be very different, requiring a technique to provide a wide detection dynamic range to allow the detection of as

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

Science.gov (United States)

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.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

Science.gov (United States)

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

MetaFIND: A feature analysis tool for metabolomics data

Directory of Open Access Journals (Sweden)

Cunningham Pádraig

2008-11-01

Full Text Available Abstract Background Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. Results In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. Conclusion Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.

MetAlign: Interface-Driven, Versatile Metabolomics Tool for Hyphenated Full-Scan Mass Spectrometry Data Preprocessing

NARCIS (Netherlands)

Lommen, A.

2009-01-01

Hyphenated full-scan MS technology creates large amounts of data. A versatile easy to handle automation tool aiding in the data analysis is very important in handling such a data stream. MetAlign software-as described in this manuscript-handles a broad range of accurate mass and nominal mass GC/MS

Metabolomics in Toxicology and Preclinical Research

Science.gov (United States)

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

Food metabolomics: from farm to human.

Science.gov (United States)

Kim, Sooah; Kim, Jungyeon; Yun, Eun Ju; Kim, Kyoung Heon

2016-02-01

Metabolomics, one of the latest components in the suite of systems biology, has been used to understand the metabolism and physiology of living systems, including microorganisms, plants, animals and humans. Food metabolomics can be defined as the application of metabolomics in food systems, including food resources, food processing and diet for humans. The study of food metabolomics has increased gradually in the recent years, because food systems are directly related to nutrition and human health. This review describes the recent trends and applications of metabolomics to food systems, from farm to human, including food resource production, industrial food processing and food intake by humans. Copyright © 2015 Elsevier Ltd. All rights reserved.

Plasma metabolomic profiling of dairy cows affected with ketosis using gas chromatography/mass spectrometry.

Science.gov (United States)

Zhang, Hongyou; Wu, Ling; Xu, Chuang; Xia, Cheng; Sun, Lingwei; Shu, Shi

2013-09-26

Ketosis is an important problem for dairy cows` production performance. However, it is still little known about plasma metabolomics details of dairy ketosis. A gas chromatography/mass spectrometry (GC/MS) technique was used to investigate plasma metabolic differences in cows that had clinical ketosis (CK, n=22), subclinical ketosis (SK, n=32), or were clinically normal controls (NC, n=22). The endogenous plasma metabolome was measured by chemical derivatization followed by GC/MS, which led to the detection of 267 variables. A two-sample t-test of 30, 32, and 13 metabolites showed statistically significant differences between SK and NC, CK and NC, and CK and SK, respectively. Orthogonal signal correction-partial least-square discriminant analysis (OPLS-DA) revealed that the metabolic patterns of both CK and SK were mostly similar, with the exception of a few differences. The development of CK and SK involved disturbances in many metabolic pathways, mainly including fatty acid metabolism, amino acid metabolism, glycolysis, gluconeogenesis, and the pentose phosphate pathway. A diagnostic model arbitrary two groups was constructed using OPLS-DA and receiver-operator characteristic curves (ROC). Multivariate statistical diagnostics yielded the 19 potential biomarkers for SK and NC, 31 for CK and NC, and 8 for CK and SK with area under the curve (AUC) values. Our results showed the potential biomarkers from CK, SK, and NC, including carbohydrates, fatty acids, amino acids, even sitosterol and vitamin E isomers, etc. 2-piperidinecarboxylic acid and cis-9-hexadecenoic acid were closely associated with metabolic perturbations in ketosis as Glc, BHBA and NEFA for dealing with metabolic disturbances of ketosis in clinical practice. However, further research is needed to explain changes of 2,3,4-trihydroxybutyric acid, 3,4-dihydroxybutyric acid, α-aminobutyric acid, methylmalonic acid, sitosterol and α-tocopherol in CK and SK, and to reveal differences between CK and SK. Our

Metabolomics er fremtiden

DEFF Research Database (Denmark)

Pedersern, Birger

2010-01-01

Forskningen i fødevarer har fået et potent redskab i hånden. Metabolomics er vejen frem, mener professor Søren Balling Engelsen fra Københavns Universitet......Forskningen i fødevarer har fået et potent redskab i hånden. Metabolomics er vejen frem, mener professor Søren Balling Engelsen fra Københavns Universitet...

Experimental Chagas disease-induced perturbations of the fecal microbiome and metabolome.

Science.gov (United States)

McCall, Laura-Isobel; Tripathi, Anupriya; Vargas, Fernando; Knight, Rob; Dorrestein, Pieter C; Siqueira-Neto, Jair L

2018-03-01

Trypanosoma cruzi parasites are the causative agents of Chagas disease. These parasites infect cardiac and gastrointestinal tissues, leading to local inflammation and tissue damage. Digestive Chagas disease is associated with perturbations in food absorption, intestinal traffic and defecation. However, the impact of T. cruzi infection on the gut microbiota and metabolome have yet to be characterized. In this study, we applied mass spectrometry-based metabolomics and 16S rRNA sequencing to profile infection-associated alterations in fecal bacterial composition and fecal metabolome through the acute-stage and into the chronic stage of infection, in a murine model of Chagas disease. We observed joint microbial and chemical perturbations associated with T. cruzi infection. These included alterations in conjugated linoleic acid (CLA) derivatives and in specific members of families Ruminococcaceae and Lachnospiraceae, as well as alterations in secondary bile acids and members of order Clostridiales. These results highlight the importance of multi-'omics' and poly-microbial studies in understanding parasitic diseases in general, and Chagas disease in particular.

Untargeted Metabolomics Approach in Halophiles: Understanding the Biodeterioration Process of Building Materials

Directory of Open Access Journals (Sweden)

Justyna Adamiak

2017-12-01

Full Text Available 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(NO32, 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.

Mono-colonization with Lactobacillus acidophilus NCFM affects the intestinal metabolome in mice

DEFF Research Database (Denmark)

Roager, Henrik Munch; Sulek, Karolina; Skov, Kasper

(NCFM) on the intestinal metabolome (jejunum, caecum, and colon) in mice by comparing NCFM mono-colonized (MC) mice with GF mice using liquid chromatography coupled to mass-spectrometry (LC-MS). The study adds to existing evidence that NCFM in vivo affects the bile acid signature of mice......-tocopherol acetate) in higher levels in the intestine of GF mice compared to MC mice, suggesting that NCFM either metabolizes the compound or indirectly affects the absorption by changing the metabolome in the intestine. The use of NCFM to increase the uptake of vitamin E supplements in humans and animals...

Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses.

Science.gov (United States)

Kazerooni, Anahita Fathi; Malek, Mahrooz; Haghighatkhah, Hamidreza; Parviz, Sara; Nabil, Mahnaz; Torbati, Leila; Assili, Sanam; Saligheh Rad, Hamidreza; Gity, Masoumeh

2017-02-01

To identify the best dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) descriptive parameters in predicting malignancy of complex ovarian masses, and develop an optimal decision tree for accurate classification of benign and malignant complex ovarian masses. Preoperative DCE-MR images of 55 sonographically indeterminate ovarian masses (27 benign and 28 malignant) were analyzed prospectively. Four descriptive parameters of the dynamic curve, namely, time-to-peak (TTP), wash-in-rate (WIR), relative signal intensity (SI rel ), and the initial area under the curve (IAUC 60 ) were calculated on the normalized curves of specified regions-of-interest (ROIs). A two-tailed Student's t-test and two automated classifiers, linear discriminant analysis (LDA) and support vector machines (SVMs), were used to compare the performance of the mentioned parameters individually and in combination with each other. TTP (P = 6.15E-8) and WIR (P = 5.65E-5) parameters induced the highest sensitivity (89% for LDA, and 97% for SVM) and specificity (93% for LDA, and 100% for SVM), respectively. Regarding the high sensitivity of TTP and high specificity of WIR and through their combination, an accurate and simple decision-tree classifier was designed using the line equation obtained by LDA classification model. The proposed classifier achieved an accuracy of 89% and area under the ROC curve of 93%. In this study an accurate decision-tree classifier based on a combination of TTP and WIR parameters was proposed, which provides a clinically flexible framework to aid radiologists/clinicians to reach a conclusive preoperative diagnosis and patient-specific therapy plan for distinguishing malignant from benign complex ovarian masses. 2 J. Magn. Reson. Imaging 2017;45:418-427. © 2016 International Society for Magnetic Resonance in Medicine.

Collision energy alteration during mass spectrometric acquisition is essential to ensure unbiased metabolomic analysis

CSIR Research Space (South Africa)

Madala, NE

2012-08-01

Full Text Available Metabolomics entails identification and quantification of all metabolites within a biological system with a given physiological status; as such, it should be unbiased. A variety of techniques are used to measure the metabolite content of living...

PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database.

Science.gov (United States)

Huang, Weiliang; Brewer, Luke K; Jones, Jace W; Nguyen, Angela T; Marcu, Ana; Wishart, David S; Oglesby-Sherrouse, Amanda G; Kane, Maureen A; Wilks, Angela

2018-01-04

The Pseudomonas aeruginosaMetabolome Database (PAMDB, http://pseudomonas.umaryland.edu) is a searchable, richly annotated metabolite database specific to P. aeruginosa. P. aeruginosa is a soil organism and significant opportunistic pathogen that adapts to its environment through a versatile energy metabolism network. Furthermore, P. aeruginosa is a model organism for the study of biofilm formation, quorum sensing, and bioremediation processes, each of which are dependent on unique pathways and metabolites. The PAMDB is modelled on the Escherichia coli (ECMDB), yeast (YMDB) and human (HMDB) metabolome databases and contains >4370 metabolites and 938 pathways with links to over 1260 genes and proteins. The database information was compiled from electronic databases, journal articles and mass spectrometry (MS) metabolomic data obtained in our laboratories. For each metabolite entered, we provide detailed compound descriptions, names and synonyms, structural and physiochemical information, nuclear magnetic resonance (NMR) and MS spectra, enzymes and pathway information, as well as gene and protein sequences. The database allows extensive searching via chemical names, structure and molecular weight, together with gene, protein and pathway relationships. The PAMBD and its future iterations will provide a valuable resource to biologists, natural product chemists and clinicians in identifying active compounds, potential biomarkers and clinical diagnostics. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Metabolomics and bioactive substances in plants

DEFF Research Database (Denmark)

Khakimov, Bekzod

Metabolomic analysis of plants broadens understanding of how plants may benefit humans, animals and the environment, provide sustainable food and energy, and improve current agricultural, pharmacological and medicinal practices in order to bring about healthier and longer life. The quality...... and amount of the extractible biological information is largely determined by data acquisition, data processing and analysis methodologies of the plant metabolomics studies. This PhD study focused mainly on the development and implementation of new metabolomics methodologies for improved data acquisition...... and data processing. The study mainly concerned the three most commonly applied analytical techniques in plant metabolomics, GC-MS, LC-MS and NMR. In addition, advanced chemometrics methods e.g. PARAFAC2 and ASCA have been extensively used for development of complex metabolomics data processing...

Diurnal effects of anoxia on the metabolome of the seagrass Zostera marina

DEFF Research Database (Denmark)

Hasler-Sheetal, Harald; Fragner, Lena; Holmer, Marianne

2015-01-01

We investigated the response, adaptation and tolerance mechanisms of the temperate seagrass Zostera marina to water column anoxia. We exposed Z. marina to a diurnal light/dark cycle under anoxia and assessed the metabolic response by measuring the metabolome with gas chromatography coupled to mass...... spectrometry (GC–MS). During anoxia and light exposure the roots showed an altered metabolome whereas the leaves were only marginally affected, indicating that photosynthetically derived oxygen could satisfy the oxygen demand in the leaves but not in the roots. Nocturnal anoxia caused a biphasic shift...... in the metabolome of roots and leaves. The first phase, after 15 h under anoxia and 3 h of darkness showed a fast increase of lactate, pyruvate, GABA (γ-aminobutyric acid), succinate, alanine and a decrease in glutamate and glutamine. The second phase, after 21 h under anoxia and 9 h of darkness showed a decrease...

Methodological aspects for metabolome visualization and characterization: a metabolomic evaluation of the 24 h evolution of human urine after cocoa powder consumption.

Science.gov (United States)

Llorach-Asunción, R; Jauregui, O; Urpi-Sarda, M; Andres-Lacueva, C

2010-01-20

The LC-MS based metabolomics studies are characterized by the capacity to produce a large and complex dataset being mandatory to use the appropriate tools to recover and to interpret as maximum information as possible. In this context, a combined partial least square discriminat analysis (PLS-DA) and two-way hierarchical clustering (two-way HCA) using Bonferroni correction as filter is proposed to improve analysis in human urinary metabolome modifications in a nutritional intervention context. After overnight fasting, 10 subjects consumed cocoa powder with milk. Urine samples were collected before the ingestion product and at 0-6, 6-12, 12-24 h after test-meal consumption and analysed by LC-Q-ToF. The PLS-DA analysis showed a clear pattern related to the differences between before consumption period and the other three periods revealing relevant mass features in this separation, however, a weaker association between mass features and the three periods after cocoa consumption was observed. On the other hand, two-way HCA showed a separation of four urine time periods and point out the mass features associated with the corresponding urine times. The correlation matrix revealed complex relations between the mass features that could be used for metabolite identifications and to infer the possible metabolite origin. The reported results prove that combining visualization strategies would be an excellent way to produce new bioinformatic applications that help the scientific community to unravel the complex relations between the consumption of phytochemicals and their expected effects on health.

Software and Database Usage on Metabolomic Studies: Using XCMS on LC-MS Data Analysis

Directory of Open Access Journals (Sweden)

Mustafa Celebier

2014-04-01

Full Text Available Metabolome is the complete set of small-molecule metabolites to be found in a cell or a single organism. Metabolomics is the scientific study to determine and identify the chemicals in metabolome with advanced analytical techniques. Nowadays, the elucidation of the molecular mechanism of any disease with genome analysis and proteome analysis is not sufficient. Instead of these, a holistic assessment including metabolomic studies provides rational and accurate results. Metabolite levels in an organism are associated with the cellular functions. Thus, determination of the metabolite amounts identifies the phenotype of a cell or tissue related with the genetic and some other variations. Even though, the analysis of metabolites for medical diagnosis and therapy have been performed for a long time, the studies to improve the analysis methods for metabolite profiling are recently increased. The application of metabolomics includes the identification of biomarkers, enzyme-substract interactions, drug-activity studies, metabolic pathway analysis and some other studies related with the system biology. The preprocessing and computing of the data obtained from LC-MS, GC-MS, CE-MS and NMR for metabolite profiling are helpful for preventing from time consuming manual data analysis processes and possible random errors on profiling period. In addition, such preprocesses allow us to identify low amount of metabolites which are not possible to be analyzed by manual processing. Therefore, the usage of software and databases for this purpose could not be ignored. In this study, it is briefly presented the software and database used on metabolomics and it is evaluated the capability of these software on metabolite profiling. Particularly, the performance of one of the most popular software called XCMS on the evaluation of LC-MS results for metabolomics was overviewed. In the near future, metabolomics with software and database support is estimated to be a routine

Metabolome Profiling of Partial and Fully Reprogrammed Induced Pluripotent Stem Cells.

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Park, Soon-Jung; Lee, Sang A; Prasain, Nutan; Bae, Daekyeong; Kang, Hyunsu; Ha, Taewon; Kim, Jong Soo; Hong, Ki-Sung; Mantel, Charlie; Moon, Sung-Hwan; Broxmeyer, Hal E; Lee, Man Ryul

2017-05-15

Acquisition of proper metabolomic fate is required to convert somatic cells toward fully reprogrammed pluripotent stem cells. The majority of induced pluripotent stem cells (iPSCs) are partially reprogrammed and have a transcriptome different from that of the pluripotent stem cells. The metabolomic profile and mitochondrial metabolic functions required to achieve full reprogramming of somatic cells to iPSC status have not yet been elucidated. Clarification of the metabolites underlying reprogramming mechanisms should enable further optimization to enhance the efficiency of obtaining fully reprogrammed iPSCs. In this study, we characterized the metabolites of human fully reprogrammed iPSCs, partially reprogrammed iPSCs, and embryonic stem cells (ESCs). Using capillary electrophoresis time-of-flight mass spectrometry-based metabolomics, we found that 89% of analyzed metabolites were similarly expressed in fully reprogrammed iPSCs and human ESCs (hESCs), whereas partially reprogrammed iPSCs shared only 74% similarly expressed metabolites with hESCs. Metabolomic profiling analysis suggested that converting mitochondrial respiration to glycolytic flux is critical for reprogramming of somatic cells into fully reprogrammed iPSCs. This characterization of metabolic reprogramming in iPSCs may enable the development of new reprogramming parameters for enhancing the generation of fully reprogrammed human iPSCs.

Metabolomics-Driven Nutraceutical Evaluation of Diverse Green Tea Cultivars

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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

Current metabolomics: technological advances.

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Putri, Sastia P; Yamamoto, Shinya; Tsugawa, Hiroshi; Fukusaki, Eiichiro

2013-07-01

Metabolomics, the global quantitative assessment of metabolites in a biological system, has played a pivotal role in various fields of science in the post-genomic era. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression, but also form part of the regulatory system in an integrated manner. Therefore, metabolomics is often considered a powerful tool to provide an instantaneous snapshot of the physiology of a cell. The power of metabolomics lies on the acquisition of analytical data in which metabolites in a cellular system are quantified, and the extraction of the most meaningful elements of the data by using various data analysis tool. In this review, we discuss the latest development of analytical techniques and data analyses methods in metabolomics study. Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

A Metabolomic Perspective on Coeliac Disease

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Calabrò, Antonio

2014-01-01

Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism. PMID:24665364

An integrated strategy for in vivo metabolite profiling using high-resolution mass spectrometry based data processing techniques

International Nuclear Information System (INIS)

Guo, Jian; Zhang, Minli; Elmore, Charles S.; Vishwanathan, Karthick

2013-01-01

Graphical abstract: -- Highlights: •Profiling the metabolites of model compounds in rats using high resolution mass spectrometry based data processing techniques. •Demonstrating an integrated strategy in vivo metabolite profiling using data mining tools. •Unusual metabolites generated via thiazole-ring opening were characterized based on processed LC–MS.data. -- Abstract: An ongoing challenge of drug metabolite profiling is to detect and identify unknown or low-level metabolites in complex biological matrices. Here we present a generic strategy for metabolite detection using multiple accurate-mass-based data processing tools via the analysis of rat samples of two model drug candidates, AZD6280 and AZ12488024. First, the function of isotopic pattern recognition was proved to be highly effective in the detection of metabolites derived from [ 14 C]-AZD6280 that possesses a distinct isotopic pattern. The metabolites revealed using this approach were in excellent qualitative correlation to those observed in radiochromatograms. Second, the effectiveness of accurate mass based untargeted data mining tools such as background subtraction, mass defect filtering, or a data mining package (MZmine) used for metabolomic analysis in detection of metabolites of [ 14 C]-AZ12488024 in rat urine, feces, bile and plasma samples was examined and a total of 33 metabolites of AZ12488024 were detected. Among them, at least 16 metabolites were only detected by the aid of the data mining packages and not via radiochromatograms. New metabolic pathways such as S-oxidation and thiomethylation reactions occurring on the thiazole ring were proposed based on the processed data. The results of these experiments also demonstrated that accurate mass-based mass defect filtering (MDF) and data mining techniques used in metabolomics are complementary and can be valuable tools for delineating low-level metabolites in complex matrices. Furthermore, the application of distinct multiple data

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

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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.

A pilot study of the effect of human breast milk on urinary metabolome analysis in infants.

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Shoji, Hiromichi; Taka, Hikari; Kaga, Naoko; Ikeda, Naho; Kitamura, Tomohiro; Miura, Yoshiki; Shimizu, Toshiaki

2017-08-28

This study aimed to examine the nutritional effect of breast feeding on healthy term infants by using urinary metabolome analysis. Urine samples were collected from 19 and 14 infants at 1 and 6 months, respectively. Infants were separated into two groups: the breast-fed group receiving metabolome analysis was performed using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS). A total of 29 metabolites were detected by CE-TOF/MS metabolome analysis in all samples. Urinary excretion of choline metabolites (choline base solution, N,N-dimethylglycine, sarcosine, and betaine) at 1 month were significantly (pmetabolome analysis by the CE-TOF/MS method is useful for assessing nutritional metabolism in infants.

Atmospheric vs. anaerobic processing of metabolome samples for the metabolite profiling of a strict anaerobic bacterium, Clostridium acetobutylicum.

Science.gov (United States)

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.

Metabolomic study of corticosterone-induced cytotoxicity in PC12 cells by ultra performance liquid chromatography-quadrupole/time-of-flight mass spectrometry.

Science.gov (United States)

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.

Comprehensive metabolomics to evaluate the impact of industrial processing on the phytochemical composition of vegetable purees

NARCIS (Netherlands)

Lopez-Sanchez, P.; Vos, de R.C.H.; Jonker, H.H.; Mumm, R.; Hall, R.D.; Bialek, L.; Leenman, R.; Strassburg, K.; Vreeken, R.; Hankemeier, T.; Schumm, S.; Duynhoven, van J.P.M.

2015-01-01

The effects of conventional industrial processing steps on global phytochemical composition of broccoli, tomato and carrot purees were investigated by using a range of complementary targeted and untargeted metabolomics approaches including LC–PDA for vitamins, 1H NMR for polar metabolites, accurate

Mass spectrometry-based metabolomic fingerprinting for screening cold tolerance in Arabidopsis thaliana accessions

Czech Academy of Sciences Publication Activity Database

Václavík, L.; Mishra, Anamika; Mishra, Kumud; Hajslova, J.

2013-01-01

Roč. 405, č. 8 (2013), s. 2671-2683 ISSN 1618-2642 R&D Projects: GA MŠk(CZ) ED1.1.00/02.0073; GA MŠk OC08055 Institutional support: RVO:67179843 Keywords : cold tolerance * Arabidopsis thaliana * metabolomic fingerprinting * LC-MS * DART-MS * chemometric analysis Subject RIV: EH - Ecology, Behaviour Impact factor: 3.578, year: 2013

Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease

Directory of Open Access Journals (Sweden)

Robert A. Quinn

2016-08-01

Full Text Available Background. Cystic fibrosis (CF is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS. The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable. Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset.

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

Science.gov (United States)

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.

NMR Techniques in Metabolomic Studies: A Quick Overview on Examples of Utilization.

Science.gov (United States)

Kruk, Joanna; Doskocz, Marek; Jodłowska, Elżbieta; Zacharzewska, Anna; Łakomiec, Joanna; Czaja, Kornelia; Kujawski, Jacek

2017-01-01

Metabolomics is a rapidly developing branch of science that concentrates on identifying biologically active molecules with potential biomarker properties. To define the best biomarkers for diseases, metabolomics uses both models (in vitro, animals) and human, as well as, various techniques such as mass spectroscopy, gas chromatography, liquid chromatography, infrared and UV-VIS spectroscopy and nuclear magnetic resonance. The last one takes advantage of the magnetic properties of certain nuclei, such as 1 H, 13 C, 31 P, 19 F, especially their ability to absorb and emit energy, what is crucial for analyzing samples. Among many spectroscopic NMR techniques not only one-dimensional (1D) techniques are known, but for many years two-dimensional (2D, for example, COSY, DOSY, JRES, HETCORE, HMQS), three-dimensional (3D, DART-MS, HRMAS, HSQC, HMBC) and solid-state NMR have been used. In this paper, authors taking apart fundamental division of nuclear magnetic resonance techniques intend to shown their wide application in metabolomic studies, especially in identifying biomarkers.

Real-time metabolome profiling of the metabolic switch between starvation and growth.

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Link, Hannes; Fuhrer, Tobias; Gerosa, Luca; Zamboni, Nicola; Sauer, Uwe

2015-11-01

Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15-30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation-induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

Directory of Open Access Journals (Sweden)

Beate Kamlage

2018-01-01

Full Text Available Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS and liquid chromatography-tandem mass spectrometry (LC-MS/MS -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased. Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA blood, EDTA plasma should be the preferred metabolomics matrix.

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.

Science.gov (United States)

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.

Metabolomic Profiles of a Midge (Procladius villosimanus, Kieffer Are Associated with Sediment Contamination in Urban Wetlands

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Katherine J. Jeppe

2017-12-01

Full Text Available Metabolomic techniques are powerful tools for investigating organism-environment interactions. Metabolite profiles have the potential to identify exposure or toxicity before populations are disrupted and can provide useful information for environmental assessment. However, under complex environmental scenarios, metabolomic responses to exposure can be distorted by background and/or organismal variation. In the current study, we use LC-MS (liquid chromatography-mass spectrometry and GC-MS (gas chromatography-mass spectrometry to measure metabolites of the midge Procladius villosimanus inhabiting 21 urban wetlands. These metabolites were tested against common sediment contaminants using random forest models and metabolite enrichment analysis. Sediment contaminant concentrations in the field correlated with several P. villosimanus metabolites despite natural environmental and organismal variation. Furthermore, enrichment analysis indicated that metabolite sets implicated in stress responses were enriched, pointing to specific cellular functions affected by exposure. Methionine metabolism, sugar metabolism and glycerolipid metabolism associated with total petroleum hydrocarbon and metal concentrations, while mitochondrial electron transport and urea cycle sets associated only with bifenthrin. These results demonstrate the potential for metabolomics approaches to provide useful information in field-based environmental assessments.

Metabolomics enables precision medicine: "A White Paper, Community Perspective".

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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

Rapid evaporative ionisation mass spectrometry and chemometrics for high-throughput screening of growth promoters in meat producing animals.

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Guitton, Yann; Dervilly-Pinel, Gaud; Jandova, Renata; Stead, Sara; Takats, Zoltan; Le Bizec, Bruno

2018-01-17

In a proof of concept perspective, Rapid Evaporative Ionisation Mass Spectrometry (REIMS) was explored for the direct analysis of meat samples from β-agonist treated livestock. In this context, the combination of REIMS with untargeted metabolomics was investigated to identify carcasses from treated animals on the basis of a modification of indirect metabolites profile. The REIMS analysis generated specific lipid profiles which enabled the differentiation of meat samples collected from pigs treated with ractopamine via their feeding regime. Furthermore, the strategy was found successful when tested on different muscle types (loin, shoulder and thigh), which further expands its applicability. Classification performances were greater than 95% accurate which fully answers requirements of a screening strategy. This research indicates that REIMS implemented in an untargeted-metabolomics workflow can be considered as a high-throughput and accurate strategy for real-time meat classification in relation to ractopamine (and wider β-agonists) treatment in pig production. This approach may subsequently be implemented as a rapid screening test, at the slaughterhouse or at border inspection points, to detect such practice.

Metabolomics Society’s International Affiliations

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Roessner, U.; Rolin, D.; Rijswijk, van M.E.C.; Hall, R.D.; Hankemeier, T.

2015-01-01

In 2012 the Metabolomics Society established a more formal system for national and regional metabolomics initiatives, interest groups, societies and networks to become an International Affiliate of the Society. A number of groups (http://metabolomicssociety.org/international-affilia

Metabolomics: the chemistry between ecology and genetics

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Macel, M.; Van Dam, N.M.; Keurentjes, J.J.B.

2010-01-01

Metabolomics is a fast developing field of comprehensive untargeted chemical analyses. It has many applications and can in principle be used on any organism without prior knowledge of the metabolome or genome. The amount of functional information that is acquired with metabolomics largely depends on

Applied metabolomics in drug discovery.

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Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Metabolomic study of lipids in serum for biomarker discovery in Alzheimer's disease using direct infusion mass spectrometry.

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González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

2014-09-01

In this study, we demonstrated the potential of direct infusion mass spectrometry for the lipidomic characterization of Alzheimer's disease. Serum samples were extracted for lipids recovery, and directly analyzed using an electrospray source. Metabolomic fingerprints were subjected to multivariate analysis in order to discriminate between groups of patients and healthy controls, and then some key-compounds were identified as possible markers of Alzheimer's disease. Major differences were found in lipids, although some low molecular weight metabolites also showed significant changes. Thus, important metabolic pathways involved in neurodegeneration could be studied on the basis of these perturbations, such as membrane breakdown (phospholipids and diacylglycerols), oxidative stress (prostaglandins, imidazole and histidine), alterations in neurotransmission systems (oleamide and putrescine) and hyperammonaemia (guanidine and arginine). Moreover, it is noteworthy that some of these potential biomarkers have not been previously described for Alzheimer's disease. Copyright © 2014 Elsevier B.V. All rights reserved.

Metabolomics of Genetically Modified Crops

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Simó, Carolina; Ibáñez, Clara; Valdés, Alberto; Cifuentes, Alejandro; García-Cañas, Virginia

2014-01-01

Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade. PMID:25334064

Metabolomics of Genetically Modified Crops

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Carolina Simó

2014-10-01

Full Text Available Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade.

Metabolomics of Hydrazine-Induced Hepatotoxicity in Rats for Discovering Potential Biomarkers

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Zhuoling An

2018-01-01

Full Text Available Metabolic pathway disturbances associated with drug-induced liver injury remain unsatisfactorily characterized. Diagnostic biomarkers for hepatotoxicity have been used to minimize drug-induced liver injury and to increase the clinical safety. A metabolomics strategy using rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS analyses and multivariate statistics was implemented to identify potential biomarkers for hydrazine-induced hepatotoxicity. The global serum and urine metabolomics of 30 hydrazine-treated rats at 24 or 48 h postdosing and 24 healthy rats were characterized by a metabolomics approach. Multivariate statistical data analyses and receiver operating characteristic (ROC curves were performed to identify the most significantly altered metabolites. The 16 most significant potential biomarkers were identified to be closely related to hydrazine-induced liver injury. The combination of these biomarkers had an area under the curve (AUC > 0.85, with 100% specificity and sensitivity, respectively. This high-quality classification group included amino acids and their derivatives, glutathione metabolites, vitamins, fatty acids, intermediates of pyrimidine metabolism, and lipids. Additionally, metabolomics pathway analyses confirmed that phenylalanine, tyrosine, and tryptophan biosynthesis as well as tyrosine metabolism had great interactions with hydrazine-induced liver injury in rats. These discriminating metabolites might be useful in understanding the pathogenesis mechanisms of liver injury and provide good prospects for drug-induced liver injury diagnosis clinically.

MetaboSearch: tool for mass-based metabolite identification using multiple databases.

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Bin Zhou

Full Text Available Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB, Madison Metabolomics Consortium Database (MMCD, Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information.The software tool is available at http://omics.georgetown.edu/MetaboSearch.html.

Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)

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Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.

2013-01-01

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum. PMID:24039616

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

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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.

NMR and MS Methods for Metabolomics.

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Amberg, Alexander; Riefke, Björn; Schlotterbeck, Götz; Ross, Alfred; Senn, Hans; Dieterle, Frank; Keck, Matthias

2017-01-01

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

Metabolomics Application in Maternal-Fetal Medicine

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Fanos, Vassilios; Atzori, Luigi; Makarenko, Karina; Melis, Gian Benedetto; Ferrazzi, Enrico

2013-01-01

Metabolomics in maternal-fetal medicine is still an “embryonic” science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, prete...

Metabolomics Application in Maternal-Fetal Medicine

Directory of Open Access Journals (Sweden)

Vassilios Fanos

2013-01-01

Full Text Available Metabolomics in maternal-fetal medicine is still an “embryonic” science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future.

Multi-platform metabolomics assays for human lung lavage fluids in an air pollution exposure study.

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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

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

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Otto Savolainen

Full Text Available 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.

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

International Nuclear Information System (INIS)

Smolinska, Agnieszka; Blanchet, Lionel; Buydens, Lutgarde M.C.; Wijmenga, Sybren S.

2012-01-01

Highlights: ► Procedures for acquisition of different biofluids by NMR. ► Recent developments in metabolic profiling of different biofluids by NMR are presented. ► The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. ► Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

A Plasma Metabolomic Signature of the Exfoliation Syndrome Involves Amino Acids, Acylcarnitines, and Polyamines.

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Leruez, Stéphanie; Bresson, Thomas; Chao de la Barca, Juan M; Marill, Alexandre; de Saint Martin, Grégoire; Buisset, Adrien; Muller, Jeanne; Tessier, Lydie; Gadras, Cédric; Verny, Christophe; Amati-Bonneau, Patrizia; Lenaers, Guy; Gohier, Philippe; Bonneau, Dominique; Simard, Gilles; Milea, Dan; Procaccio, Vincent; Reynier, Pascal

2018-02-01

To determine the plasma metabolomic signature of the exfoliative syndrome (XFS), the most common cause worldwide of secondary open-angle glaucoma. We performed a targeted metabolomic study, using the standardized p180 Biocrates Absolute IDQ p180 kit with a QTRAP 5500 mass spectrometer, to compare the metabolomic profiles of plasma from individuals with XFS (n = 16), and an age- and sex-matched control group with cataract (n = 18). A total of 151 metabolites were detected correctly, 16 of which allowed for construction of an OPLS-DA model with a good predictive capability (Q2cum = 0.51) associated with a low risk of over-fitting (permQ2 = -0.48, CV-ANOVA P-value <0.001). The metabolites contributing the most to the signature were octanoyl-carnitine (C8) and decanoyl-carnitine (C10), the branched-chain amino acids (i.e., isoleucine, leucine, and valine), and tyrosine, all of which were at higher concentrations in the XFS group, whereas spermine and spermidine, together with their precursor acetyl-ornithine, were at lower concentrations than in the control group. We identified a significant metabolomic signature in the plasma of individuals with XFS. Paradoxically, this signature, characterized by lower concentrations of the neuroprotective spermine and spermidine polyamines than in controls, partially overlaps the plasma metabolomic profile associated with insulin resistance, despite the absence of evidence of insulin resistance in XFS.

Vitroprocines, new antibiotics against Acinetobacter baumannii, discovered from marine Vibrio sp. QWI-06 using mass-spectrometry-based metabolomics approach

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Liaw, Chih-Chuang; Chen, Pei-Chin; Shih, Chao-Jen; Tseng, Sung-Pin; Lai, Ying-Mi; Hsu, Chi-Hsin; Dorrestein, Pieter C.; Yang, Yu-Liang

2015-08-01

A robust and convenient research strategy integrating state-of-the-art analytical techniques is needed to efficiently discover novel compounds from marine microbial resources. In this study, we identified a series of amino-polyketide derivatives, vitroprocines A-J, from the marine bacterium Vibrio sp. QWI-06 by an integrated approach using imaging mass spectroscopy and molecular networking, as well as conventional bioactivity-guided fractionation and isolation. The structure-activity relationship of vitroprocines against Acinetobacter baumannii is proposed. In addition, feeding experiments with 13C-labeled precursors indicated that a pyridoxal 5‧-phosphate-dependent mechanism is involved in the biosynthesis of vitroprocines. Elucidation of amino-polyketide derivatives from a species of marine bacteria for the first time demonstrates the potential of this integrated metabolomics approach to uncover marine bacterial biodiversity.

Functional metabolomics reveals novel active products in the DHA metabolome

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Masakazu eShinohara

2012-04-01

Full Text Available Endogenous mechanisms for successful resolution of an acute inflammatory response and the local return to homeostasis are of interest because excessive inflammation underlies many human diseases. In this review, we provide an update and overview of functional metabolomics that identified a new bioactive metabolome of docosahexaenoic acid (DHA. Systematic studies revealed that DHA was converted to DHEA-derived novel bioactive products as well as aspirin-triggered (AT forms of protectins. The new oxygenated DHEA derived products blocked PMN chemotaxis, reduced P-selectin expression and platelet-leukocyte adhesion, and showed organ protection in ischemia/reperfusion injury. These products activated cannabinoid receptor (CB2 receptor and not CB1 receptors. The AT-PD1 reduced neutrophil (PMN recruitment in murine peritonitis. With human cells, AT-PD1 decreased transendothelial PMN migration as well as enhanced efferocytosis of apoptotic human PMN by macrophages. The recent findings reviewed here indicate that DHEA oxidative metabolism and aspirin-triggered conversion of DHA produce potent novel molecules with anti-inflammatory and organ-protective properties, opening the DHA metabolome functional roles.

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

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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

Symbiosis of chemometrics and metabolomics: past, present, and future

NARCIS (Netherlands)

van der Greef, J.; Smilde, A. K.

2005-01-01

Metabolomics is a growing area in the field of systems biology. Metabolomics has already a long history and also the connection of metabolomics with chemometrics goes back some time. This review discusses the symbiosis of metabolomics and chemometrics with emphasis on the medical domain, puts the

Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow.

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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.

Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines

International Nuclear Information System (INIS)

Arbulu, M.; Sampedro, M.C.; Gómez-Caballero, A.; Goicolea, M.A.; Barrio, R.J.

2015-01-01

Highlights: • An untargeted metabolomic method for the non-volatile profile of the Graciano wine was developed. • 411 different metabolites in Graciano Vitis vinifera red wine were identified. • 15 compounds could serve to differentiate Graciano and Tempranillo wines. • An enological database (WinMet) with 2080 compounds was constructed. - Abstract: The current study presents a method for comprehensive untargeted metabolomic fingerprinting of the non-volatile profile of the Graciano Vitis vinifera wine variety, using liquid chromatography/electrospray ionization time of flight mass spectrometry (LC–ESI-QTOF). Pre-treatment of samples, chromatographic columns, mobile phases, elution gradients and ionization sources, were evaluated for the extraction of the maximum number of metabolites in red wine. Putative compounds were extracted from the raw data using the extraction algorithm, molecular feature extractor (MFE). For the metabolite identification the WinMet database was designed based on electronic databases and literature research and includes only the putative metabolites reported to be present in oenological matrices. The results from WinMet were compared with those in the METLIN database to evaluate how much the databases overlap for performing identifications. The reproducibility of the analysis was assessed using manual processing following replicate injections of Vitis vinifera cv. Graciano wine spiked with external standards. In the present work, 411 different metabolites in Graciano Vitis vinifera red wine were identified, including primary wine metabolites such as sugars (4%), amino acids (23%), biogenic amines (4%), fatty acids (2%), and organic acids (32%) and secondary metabolites such as phenols (27%) and esters (8%). Significant differences between varieties Tempranillo and Graciano were related to the presence of fifteen specific compounds

Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines

Energy Technology Data Exchange (ETDEWEB)

Arbulu, M. [Department of Analytical Chemistry, Faculty of Pharmacy, University of the Basque Country, 01006 Vitoria-Gasteiz (Spain); Sampedro, M.C. [Central Service of Analysis, SGIker, University of the Basque Country, 01006 Vitoria-Gasteiz (Spain); Gómez-Caballero, A.; Goicolea, M.A. [Department of Analytical Chemistry, Faculty of Pharmacy, University of the Basque Country, 01006 Vitoria-Gasteiz (Spain); Barrio, R.J., E-mail: r.barrio@ehu.es [Department of Analytical Chemistry, Faculty of Pharmacy, University of the Basque Country, 01006 Vitoria-Gasteiz (Spain)

2015-02-09

Highlights: • An untargeted metabolomic method for the non-volatile profile of the Graciano wine was developed. • 411 different metabolites in Graciano Vitis vinifera red wine were identified. • 15 compounds could serve to differentiate Graciano and Tempranillo wines. • An enological database (WinMet) with 2080 compounds was constructed. - Abstract: The current study presents a method for comprehensive untargeted metabolomic fingerprinting of the non-volatile profile of the Graciano Vitis vinifera wine variety, using liquid chromatography/electrospray ionization time of flight mass spectrometry (LC–ESI-QTOF). Pre-treatment of samples, chromatographic columns, mobile phases, elution gradients and ionization sources, were evaluated for the extraction of the maximum number of metabolites in red wine. Putative compounds were extracted from the raw data using the extraction algorithm, molecular feature extractor (MFE). For the metabolite identification the WinMet database was designed based on electronic databases and literature research and includes only the putative metabolites reported to be present in oenological matrices. The results from WinMet were compared with those in the METLIN database to evaluate how much the databases overlap for performing identifications. The reproducibility of the analysis was assessed using manual processing following replicate injections of Vitis vinifera cv. Graciano wine spiked with external standards. In the present work, 411 different metabolites in Graciano Vitis vinifera red wine were identified, including primary wine metabolites such as sugars (4%), amino acids (23%), biogenic amines (4%), fatty acids (2%), and organic acids (32%) and secondary metabolites such as phenols (27%) and esters (8%). Significant differences between varieties Tempranillo and Graciano were related to the presence of fifteen specific compounds.

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

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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 to unveil and understand phenotypic diversity between pathogen populations.

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Ruben t'Kindt

Full Text Available Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.

An R package for the integrated analysis of metabolomics and spectral data.

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Costa, Christopher; Maraschin, Marcelo; Rocha, Miguel

2016-06-01

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as nuclear magnetic resonance, gas or liquid chromatography, mass spectrometry, infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Metabolomic applications in radiation biodosimetry: exploring radiation effects through small molecules.

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Pannkuk, Evan L; Fornace, Albert J; Laiakis, Evagelia C

2017-10-01

Exposure of the general population to ionizing radiation has increased in the past decades, primarily due to long distance travel and medical procedures. On the other hand, accidental exposures, nuclear accidents, and elevated threats of terrorism with the potential detonation of a radiological dispersal device or improvised nuclear device in a major city, all have led to increased needs for rapid biodosimetry and assessment of exposure to different radiation qualities and scenarios. Metabolomics, the qualitative and quantitative assessment of small molecules in a given biological specimen, has emerged as a promising technology to allow for rapid determination of an individual's exposure level and metabolic phenotype. Advancements in mass spectrometry techniques have led to untargeted (discovery phase, global assessment) and targeted (quantitative phase) methods not only to identify biomarkers of radiation exposure, but also to assess general perturbations of metabolism with potential long-term consequences, such as cancer, cardiovascular, and pulmonary disease. Metabolomics of radiation exposure has provided a highly informative snapshot of metabolic dysregulation. Biomarkers in easily accessible biofluids and biospecimens (urine, blood, saliva, sebum, fecal material) from mouse, rat, and minipig models, to non-human primates and humans have provided the basis for determination of a radiation signature to assess the need for medical intervention. Here we provide a comprehensive description of the current status of radiation metabolomic studies for the purpose of rapid high-throughput radiation biodosimetry in easily accessible biofluids and discuss future directions of radiation metabolomics research.

Comparative mass spectrometry & nuclear magnetic resonance metabolomic approaches for nutraceuticals quality control analysis: a brief review.

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Farag, Mohamed A

2014-01-01

The number of botanical dietary supplements in the market has recently increased primarily due to increased health awareness. Standardization and quality control of the constituents of these plant extracts is an important topic, particularly when such ingredients are used long term as dietary supplements, or in cases where higher doses are marketed as drugs. The development of fast, comprehensive, and effective untargeted analytical methods for plant extracts is of high interest. Nuclear magnetic resonance spectroscopy and mass spectrometry are the most informative tools, each of which enables high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in the majority of plant metabolomics applications, there is a growing interest in combining the data from both platforms to effectively unravel the complexity of plant samples. The application of combined MS and NMR in the quality control of nutraceuticals forms the major part of this review. Finally I will look at the future developments and perspectives of these two technologies for the quality control of herbal materials.

An overview of renal metabolomics.

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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.

Mass Spectrometry-Based Quantitative Metabolomics Revealed a Distinct Lipid Profile in Breast Cancer Patients

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Yun Yen

2013-04-01

Full Text Available Breast cancer accounts for the largest number of newly diagnosed cases in female cancer patients. Although mammography is a powerful screening tool, about 20% of breast cancer cases cannot be detected by this method. New diagnostic biomarkers for breast cancer are necessary. Here, we used a mass spectrometry-based quantitative metabolomics method to analyze plasma samples from 55 breast cancer patients and 25 healthy controls. A number of 30 patients and 20 age-matched healthy controls were used as a training dataset to establish a diagnostic model and to identify potential biomarkers. The remaining samples were used as a validation dataset to evaluate the predictive accuracy for the established model. Distinct separation was obtained from an orthogonal partial least squares-discriminant analysis (OPLS-DA model with good prediction accuracy. Based on this analysis, 39 differentiating metabolites were identified, including significantly lower levels of lysophosphatidylcholines and higher levels of sphingomyelins in the plasma samples obtained from breast cancer patients compared with healthy controls. Using logical regression, a diagnostic equation based on three metabolites (lysoPC a C16:0, PC ae C42:5 and PC aa C34:2 successfully differentiated breast cancer patients from healthy controls, with a sensitivity of 98.1% and a specificity of 96.0%.

Impact of Soil Warming on the Plant Metabolome of Icelandic Grasslands

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Gargallo-Garriga, Albert; Ayala-Roque, Marta; Granda, Victor; Sigurdsson, Bjarni D.; Leblans, Niki I. W.; Oravec, Michal; Urban, Otmar; Janssens, Ivan A.

2017-01-01

Climate change is stronger at high than at temperate and tropical latitudes. The natural geothermal conditions in southern Iceland provide an opportunity to study the impact of warming on plants, because of the geothermal bedrock channels that induce stable gradients of soil temperature. We studied two valleys, one where such gradients have been present for centuries (long-term treatment), and another where new gradients were created in 2008 after a shallow crustal earthquake (short-term treatment). We studied the impact of soil warming (0 to +15 °C) on the foliar metabolomes of two common plant species of high northern latitudes: Agrostis capillaris, a monocotyledon grass; and Ranunculus acris, a dicotyledonous herb, and evaluated the dependence of shifts in their metabolomes on the length of the warming treatment. The two species responded differently to warming, depending on the length of exposure. The grass metabolome clearly shifted at the site of long-term warming, but the herb metabolome did not. The main up-regulated compounds at the highest temperatures at the long-term site were saccharides and amino acids, both involved in heat-shock metabolic pathways. Moreover, some secondary metabolites, such as phenolic acids and terpenes, associated with a wide array of stresses, were also up-regulated. Most current climatic models predict an increase in annual average temperature between 2–8 °C over land masses in the Arctic towards the end of this century. The metabolomes of A. capillaris and R. acris shifted abruptly and nonlinearly to soil warming >5 °C above the control temperature for the coming decades. These results thus suggest that a slight warming increase may not imply substantial changes in plant function, but if the temperature rises more than 5 °C, warming may end up triggering metabolic pathways associated with heat stress in some plant species currently dominant in this region. PMID:28832555

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.

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Myers, Owen D; Sumner, Susan J; Li, Shuzhao; Barnes, Stephen; Du, Xiuxia

2017-09-05

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

Exploring the Metabolomic Responses of Bacillus licheniformis to Temperature Stress by Gas Chromatography/Mass Spectrometry.

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Dong, Zixing; Chen, Xiaoling; Cai, Ke; Chen, Zhixin; Wang, Hongbin; Jin, Peng; Liu, Xiaoguang; Permaul, Kugenthiren; Singh, Suren; Wang, Zhengxiang

2018-03-28

Owing to its high protein secretion capacity, simple nutritional requirements, and GRAS (generally regarded as safe) status, Bacillus licheniformis is widely used as a host for the industrial production of enzymes, antibiotics, and peptides. However, as compared with its close relative Bacillus subtilis , little is known about the physiology and stress responses of B. licheniformis . To explore its temperature-stress metabolome, B. licheniformis strains ATCC 14580 and B186, with respective optimal growth temperatures of 42°C and 50°C, were cultured at 42°C, 50°C, and 60°C and their corresponding metabolic profiles were determined by gas chromatography/mass spectrometry and multivariate statistical analyses. It was found that with increased growth temperatures, the two B. licheniformis strains displayed elevated cellular levels of proline, glutamate, lysine, pentadecanoic acid, hexadecanoic acid, heptadecanoic acid, and octadecanoic acid, and decreased levels of glutamine and octadecenoic acid. Regulation of amino acid and fatty acid metabolism is likely to be associated with the evolution of protective biochemical mechanisms of B. licheniformis . Our results will help to optimize the industrial use of B. licheniformis and other important Bacillus species.

Screening in veterinary drug analysis and sports doping control based on full-scan, accurate-mass spectrometry

NARCIS (Netherlands)

Peters, R.J.B.; Stolker, A.A.M.; Mol, J.G.J.; Lommen, A.; Lyris, E.; Angelis, Y.S.; Vonaparti, A.; Stamou, M.; Georgakopoulos, C.G.; Nielen, M.W.F.

2010-01-01

A common trend in food contaminants and sports doping control is towards a limited number of targeted, full-scan, accurate-mass spectrometry (MS) methods based on time-of-flight (TOF) or Fourier-transform orbital trap (Orbitrap) mass analyzers. Retrospective analysis of the full-scan datasets of

Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges.

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Töpfer, Nadine; Seaver, Samuel M D; Aharoni, Asaph

2018-01-01

In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.

Metabolomics: beyond biomarkers and towards mechanisms

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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 effects of age and dietary restriction on the tissue-specific metabolome of Drosophila.

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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.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges.

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Tokarz, Janina; Haid, Mark; Cecil, Alexander; Prehn, Cornelia; Artati, Anna; Möller, Gabriele; Adamski, Jerzy

2017-10-01

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now. Copyright © 2017 Elsevier Ltd. All rights reserved.

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Energy Technology Data Exchange (ETDEWEB)

Smolinska, Agnieszka, E-mail: A.Smolinska@science.ru.nl [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Blanchet, Lionel [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Department of Biochemistry, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen (Netherlands); Buydens, Lutgarde M.C.; Wijmenga, Sybren S. [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands)

2012-10-31

Highlights: Black-Right-Pointing-Pointer Procedures for acquisition of different biofluids by NMR. Black-Right-Pointing-Pointer Recent developments in metabolic profiling of different biofluids by NMR are presented. Black-Right-Pointing-Pointer The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. Black-Right-Pointing-Pointer Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

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Joanna Hajduk

2015-12-01

Full Text Available 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.

Metabolomic biomarkers correlating with hepatic lipidosis in dairy cows.

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Imhasly, Sandro; Naegeli, Hanspeter; Baumann, Sven; von Bergen, Martin; Luch, Andreas; Jungnickel, Harald; Potratz, Sarah; Gerspach, Christian

2014-06-02

Hepatic lipidosis or fatty liver disease is a major metabolic disorder of high-producing dairy cows that compromises animal performance and, hence, causes heavy economic losses worldwide. This syndrome, occurring during the critical transition from gestation to early lactation, leads to an impaired health status, decreased milk yield, reduced fertility and shortened lifetime. Because the prevailing clinical chemistry parameters indicate advanced liver damage independently of the underlying disease, currently, hepatic lipidosis can only be ascertained by liver biopsy. We hypothesized that the condition of fatty liver disease may be accompanied by an altered profile of endogenous metabolites in the blood of affected animals. To identify potential small-molecule biomarkers as a novel diagnostic alternative, the serum samples of diseased dairy cows were subjected to a targeted metabolomics screen by triple quadrupole mass spectrometry. A subsequent multivariate test involving principal component and linear discriminant analyses yielded 29 metabolites (amino acids, phosphatidylcholines and sphingomyelines) that, in conjunction, were able to distinguish between dairy cows with no hepatic lipidosis and those displaying different stages of the disorder. This proof-of-concept study indicates that metabolomic profiles, including both amino acids and lipids, distinguish hepatic lipidosis from other peripartal disorders and, hence, provide a promising new tool for the diagnosis of hepatic lipidosis. By generating insights into the molecular pathogenesis of hepatic lipidosis, metabolomics studies may also facilitate the prevention of this syndrome.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.

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Karsten Suhre

Full Text Available BACKGROUND: Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. METHODOLOGY/PRINCIPAL FINDINGS: 40 individuals with self-reported diabetes and 60 controls (male, over 54 years were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol, ketone bodies (3-hydroxybutyrate, and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid. CONCLUSIONS/SIGNIFICANCE: Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate, lipid metabolism (glycerophospholipids, free fatty acids, and interaction with the gut microflora (bile acids. Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

DEFF Research Database (Denmark)

Huang, Sijia; Chong, Nicole; Lewis, Nathan

2016-01-01

diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...

Probabilistic Principal Component Analysis for Metabolomic Data.

LENUS (Irish Health Repository)

Nyamundanda, Gift

2010-11-23

Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

MIDAS: a database-searching algorithm for metabolite identification in metabolomics.

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Wang, Yingfeng; Kora, Guruprasad; Bowen, Benjamin P; Pan, Chongle

2014-10-07

A database searching approach can be used for metabolite identification in metabolomics by matching measured tandem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways, and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite's predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution tandem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in an automated and high-throughput manner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of standard tandem mass spectra from MassBank. On average, for 77% of original spectra and 84% of composite spectra, MIDAS correctly ranked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra and 59% more composite spectra at the first MSMs than an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified many metabolites missed in the previous study. MIDAS identifications should be considered only as candidate metabolites, which need to be confirmed using standard compounds. To facilitate manual validation, MIDAS provides annotated spectra for MSMs and labels observed mass spectral peaks with predicted fragments. The database searching and manual validation can be performed online at http://midas.omicsbio.org.

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

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Fidele Tugizimana

2016-11-01

Full Text Available Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen were used. Raw data were pre-processed with MarkerLynxTM software (Waters Corporation, Manchester, UK. Here, two parameters were varied: the intensity threshold (50–100 counts and the mass tolerance (0.005–0.01 Da. After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc. and data transformation (log and power methods were explored. The results showed that the pre-processing parameters (or algorithms influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables. Thus, as informed by the results, to maximize the value of untargeted metabolomic data

Sulfites and the wine metabolome.

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Roullier-Gall, Chloé; Hemmler, Daniel; Gonsior, Michael; Li, Yan; Nikolantonaki, Maria; Aron, Alissa; Coelho, Christian; Gougeon, Régis D; Schmitt-Kopplin, Philippe

2017-12-15

In a context of societal concern about food preservation, the reduction of sulfite input plays a major role in the wine industry. To improve the understanding of the chemistry involved in the SO 2 protection, a series of bottle aged Chardonnay wines made from the same must, but with different concentrations of SO 2 added at pressing were analyzed by ultrahigh resolution mass spectrometry (FT-ICR-MS) and excitation emission matrix fluorescence (EEMF). Metabolic fingerprints from FT-ICR-MS data could discriminate wines according to the added concentration to the must but they also revealed chemistry-related differences according to the type of stopper, providing a wine metabolomics picture of the impact of distinct stopping strategies. Spearman rank correlation was applied to link the statistically modeled EEMF components (parallel factor analysis (PARAFAC)) and the exact mass information from FT-ICR-MS, and thus revealing the extent of sulfur-containing compounds which could show some correlation with fluorescence fingerprints. Copyright © 2017 Elsevier Ltd. All rights reserved.

Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?

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Chen, Jiaqing; Zhang, Pei; Lv, Mengying; Guo, Huimin; Huang, Yin; Zhang, Zunjian; Xu, Fengguo

2017-05-16

Data reduction techniques in gas chromatography-mass spectrometry-based untargeted metabolomics has made the following workflow of data analysis more lucid. However, the normalization process still perplexes researchers, and its effects are always ignored. In order to reveal the influences of normalization method, five representative normalization methods (mass spectrometry total useful signal, median, probabilistic quotient normalization, remove unwanted variation-random, and systematic ratio normalization) were compared in three real data sets with different types. First, data reduction techniques were used to refine the original data. Then, quality control samples and relative log abundance plots were utilized to evaluate the unwanted variations and the efficiencies of normalization process. Furthermore, the potential biomarkers which were screened out by the Mann-Whitney U test, receiver operating characteristic curve analysis, random forest, and feature selection algorithm Boruta in different normalized data sets were compared. The results indicated the determination of the normalization method was difficult because the commonly accepted rules were easy to fulfill but different normalization methods had unforeseen influences on both the kind and number of potential biomarkers. Lastly, an integrated strategy for normalization method selection was recommended.

Mathematical Modeling Approaches in Plant Metabolomics.

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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.

Identifying biomarkers for asthma diagnosis using targeted metabolomics approaches.

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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  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.

New biomarkers of coffee consumption identified by the non-targeted metabolomic profiling of cohort study subjects.

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Joseph A Rothwell

Full Text Available Coffee contains various bioactives implicated with human health and disease risk. To accurately assess the effects of overall consumption upon health and disease, individual intake must be measured in large epidemiological studies. Metabolomics has emerged as a powerful approach to discover biomarkers of intake for a large range of foods. Here we report the profiling of the urinary metabolome of cohort study subjects to search for new biomarkers of coffee intake. Using repeated 24-hour dietary records and a food frequency questionnaire, 20 high coffee consumers (183-540 mL/d and 19 low consumers were selected from the French SU.VI.MAX2 cohort. Morning spot urine samples from each subject were profiled by high-resolution mass spectrometry. Partial least-square discriminant analysis of multidimensional liquid chromatography-mass spectrometry data clearly distinguished high consumers from low via 132 significant (p-value<0.05 discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and in-house databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl, and the alkaloid trigonelline. Some caffeine metabolites, such as 1-methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity. Once validated in other cohorts or intervention studies, these specific single or combined biomarkers will become a valuable alternative to assessment of coffee intake by dietary survey and finally lead to a better understanding of

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

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Bruno, C; Patin, F; Bocca, C; Nadal-Desbarats, L; Bonnier, F; Reynier, P; Emond, P; Vourc'h, P; Joseph-Delafont, K; Corcia, P; Andres, C R; Blasco, H

2018-01-30

Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering. We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained. Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

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Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

2016-08-18

A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed

Radiation Metabolomics: Current Status and Future Directions

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Smrithi eSugumaran Menon

2016-02-01

Full Text Available Human exposure to ionizing radiation disrupts normal metabolic processes in cells and organs by inducing complex biological responses that interfere with gene and protein expression. Conventional dosimetry, monitoring of prodromal symptoms and peripheral lymphocyte counts are of limited value as organ and tissue specific biomarkers for personnel exposed to radiation, particularly, weeks or months after exposure. Analysis of metabolites generated in known stress-responsive pathways by molecular profiling helps to predict the physiological status of an individual in response to environmental or genetic perturbations. Thus, a multi-metabolite profile obtained from a high resolution mass spectrometry-based metabolomics platform offers potential for identification of robust biomarkers to predict radiation toxicity of organs and tissues resulting from exposures to therapeutic or non-therapeutic ionizing radiation. Here, we review the status of radiation metabolomics and explore applications as a standalone technology, as well as its integration in systems biology, to facilitate a better understanding of the molecular basis of radiation response. Finally, we draw attention to the identification of specific pathways that can be targeted for the development of therapeutics to alleviate or mitigate harmful effects of radiation exposure.

Updates in metabolomics tools and resources: 2014-2015.

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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.

Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine.

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Edmands, William M B; Ferrari, Pietro; Scalbert, Augustin

2014-11-04

Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.

Metabolomics and Personalized Medicine.

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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.

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles.

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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.

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

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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.

Metabolomics through the lens of precision cardiovascular medicine.

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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.

Metabolomic assessment reveals a stimulatory effect of calcium treatment on glucosinolates contents in broccoli microgreen

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Preharvest calcium application has been shown to increase broccoli microgreen yield and extend shelf life. Here we investigated the effect of calcium application on its metabolome using ultra high-performance liquid chromatography (UHPLC) tandem with mass spectrometry (HRMS). The data collected were...

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

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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.

Accurate screening for synthetic preservatives in beverage using high performance liquid chromatography with time-of-flight mass spectrometry

International Nuclear Information System (INIS)

Li Xiuqin; Zhang Feng; Sun Yanyan; Yong Wei; Chu Xiaogang; Fang Yanyan; Zweigenbaum, Jerry

2008-01-01

In this study, liquid chromatography time-of-flight mass spectrometry (HPLC/TOF-MS) is applied to qualitation and quantitation of 18 synthetic preservatives in beverage. The identification by HPLC/TOF-MS is accomplished with the accurate mass (the subsequent generated empirical formula) of the protonated molecules [M + H]+ or the deprotonated molecules [M - H]-, along with the accurate mass of their main fragment ions. In order to obtain sufficient sensitivity for quantitation purposes (using the protonated or deprotonated molecule) and additional qualitative mass spectrum information provided by the fragments ions, segment program of fragmentor voltages is designed in positive and negative ion mode, respectively. Accurate mass measurements are highly useful in the complex sample analyses since they allow us to achieve a high degree of specificity, often needed when other interferents are present in the matrix. The mass accuracy typically obtained is routinely better than 3 ppm. The 18 compounds behave linearly in the 0.005-5.0 mg.kg -1 concentration range, with correlation coefficient >0.996. The recoveries at the tested concentrations of 1.0 mg.kg -1 -100 mg.kg -1 are 81-106%, with coefficients of variation -1 , which are far below the required maximum residue level (MRL) for these preservatives in foodstuff. The method is suitable for routine quantitative and qualitative analyses of synthetic preservatives in foodstuff

Cartilaginous Metabolomic Study Reveals Potential Mechanisms of Osteophyte Formation in Osteoarthritis.

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Xu, Zhongwei; Chen, Tingmei; Luo, Jiao; Ding, Shijia; Gao, Sichuan; Zhang, Jian

2017-04-07

Osteophyte is one of the inevitable consequences of progressive osteoarthritis with the main characteristics of cartilage degeneration and endochondral ossification. The pathogenesis of osteophyte formation is not fully understood to date. In this work, metabolomic approaches were employed to explore potential mechanisms of osteophyte formation by detecting metabolic variations between extracts of osteophyte cartilage tissues (n = 32) and uninvolved control cartilage tissues (n = 34), based on the platform of ultraperformance liquid chromatography tandem quadrupole time-of-flight mass spectrometry, as well as the use of multivariate statistic analysis and univariate statistic analysis. The osteophyte group was significantly separated from the control group by the orthogonal partial least-squares discriminant analysis models, indicating that metabolic state of osteophyte cartilage had been changed. In total, 28 metabolic variations further validated by mass spectrum (MS) match, tandom mass spectrum (MS/MS) match, and standards match mainly included amino acids, sulfonic acids, glycerophospholipids, and fatty acyls. These metabolites were related to some specific physiological or pathological processes (collagen dissolution, boundary layers destroyed, self-restoration triggered, etc.) which might be associated with the procedure of osteophyte formation. Pathway analysis showed phenylalanine metabolism (PI = 0.168, p = 0.004) was highly correlative to this degenerative process. Our findings provided a direction for targeted metabolomic study and an insight into further reveal the molecular mechanisms of ostophyte formation.

The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy.

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Brian H Walsh

Full Text Available Hypoxic ischaemic encephalopathy (HIE in newborns can cause significant long-term neurological disability. The insult is a complex injury characterised by energy failure and disruption of cellular homeostasis, leading to mitochondrial damage. The importance of individual metabolic pathways, and their interaction in the disease process is not fully understood. The aim of this study was to describe and quantify the metabolomic profile of umbilical cord blood samples in a carefully defined population of full-term infants with HIE.The injury severity was defined using both the modified Sarnat score and continuous multichannel electroencephalogram. Using these classification systems, our population was divided into those with confirmed HIE (n = 31, asphyxiated infants without encephalopathy (n = 40 and matched controls (n = 71. All had umbilical cord blood drawn and biobanked at -80 °C within 3 hours of delivery. A combined direct injection and LC-MS/MS assay (AbsolutIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria was used for the metabolomic analyses of the samples. Targeted metabolomic analysis showed a significant alteration between study groups in 29 metabolites from 3 distinct classes (Amino Acids, Acylcarnitines, and Glycerophospholipids. 9 of these metabolites were only significantly altered between neonates with Hypoxic ischaemic encephalopathy and matched controls, while 14 were significantly altered in both study groups. Multivariate Discriminant Analysis models developed showed clear multifactorial metabolite associations with both asphyxia and HIE. A logistic regression model using 5 metabolites clearly delineates severity of asphyxia and classifies HIE infants with AUC = 0.92. These data describe wide-spread disruption to not only energy pathways, but also nitrogen and lipid metabolism in both asphyxia and HIE.This study shows that a multi-platform targeted approach to metabolomic analyses using accurately phenotyped and

Metabolomics applied to the pancreatic islet.

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Gooding, Jessica R; Jensen, Mette V; Newgard, Christopher B

2016-01-01

Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Metabolomic applications to decipher gut microbial metabolic influence in health and disease

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Francois-Pierre eMartin

2012-04-01

Full Text Available Dietary preferences and nutrients composition have been shown to influence human and gut microbial metabolism, which ultimately has specific effects on health and diseases’ risk. Increasingly, results from molecular biology and microbiology demonstrate the key role of the gut microbiota metabolic interface to the overall mammalian host’s health status. There is therefore raising interest in nutrition research to characterize the molecular foundations of the gut microbial mammalian cross-talk at both physiological and biochemical pathway levels. Tackling these challenges can be achieved through systems biology approaches, such as metabolomics, to underpin the highly complex metabolic exchanges between diverse biological compartments, including organs, systemic biofluids and microbial symbionts. By the development of specific biomarkers for prediction of health and disease, metabolomics is increasingly used in clinical applications as regard to disease aetiology, diagnostic stratification and potentially mechanism of action of therapeutical and nutraceutical solutions. Surprisingly, an increasing number of metabolomics investigations in pre-clinical and clinical studies based on proton nuclear magnetic resonance (1H NMR spectroscopy and mass spectrometry (MS provided compelling evidence that system wide and organ-specific biochemical processes are under the influence of gut microbial metabolism. This review aims at describing recent applications of metabolomics in clinical fields where main objective is to discern the biochemical mechanisms under the influence of the gut microbiota, with insight into gastrointestinal health and diseases diagnostics and improvement of homeostasis metabolic regulation.

Metabolomic Profiling of Plasma from Patients with Tuberculosis by Use of Untargeted Mass Spectrometry Reveals Novel Biomarkers for Diagnosis.

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Lau, Susanna K P; Lee, Kim-Chung; Curreem, Shirly O T; Chow, Wang-Ngai; To, Kelvin K W; Hung, Ivan F N; Ho, Deborah T Y; Sridhar, Siddharth; Li, Iris W S; Ding, Vanessa S Y; Koo, Eleanor W F; Wong, Chi-Fong; Tam, Sidney; Lam, Ching-Wan; Yuen, Kwok-Yung; Woo, Patrick C Y

2015-12-01

Although tuberculosis (TB) is a reemerging disease that affects people in developing countries and immunocompromised populations in developed countries, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used for studies on infectious diseases. We performed metabolome profiling of plasma samples to identify potential biomarkers for diagnosing TB. We compared the plasma metabolome profiles of TB patients (n = 46) with those of community-acquired pneumonia (CAP) patients (n = 30) and controls without active infection (n = 30) using ultrahigh-performance liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry (UHPLC-ESI-QTOFMS). Using multivariate and univariate analyses, four metabolites, 12R-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid [12(R)-HETE], ceramide (d18:1/16:0), cholesterol sulfate, and 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol, were identified and found to have significantly higher levels in TB patients than those in CAP patients and controls. In a comparison of TB patients and controls, the four metabolites demonstrated area under the receiver operating characteristic curve (AUC) values of 0.914, 0.912, 0.905, and 0.856, sensitivities of 84.8%, 84.8%, 87.0%, and 89.1%, specificities of 90.0%, 86.7%, 86.7%, and 80.0%, and fold changes of 4.19, 26.15, 6.09, and 1.83, respectively. In a comparison of TB and CAP patients, the four metabolites demonstrated AUC values of 0.793, 0.717, 0.802, and 0.894, sensitivities of 89.1%, 71.7%, 80.4%, and 84.8%, specificities of 63.3%, 66.7%, 70.0%, and 83.3%, and fold changes of 4.69, 3.82, 3.75, and 2.16, respectively. 4α-Formyl-4β-methyl-5α-cholesta-8-en-3β-ol combined with 12(R)-HETE or cholesterol sulfate offered ≥70% sensitivity and ≥90% specificity for differentiating TB patients from controls or CAP patients. These novel plasma biomarkers, especially 12(R)-HETE and 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol, alone or in

Tools and databases of the KOMICS web portal for preprocessing, mining, and dissemination of metabolomics data.

Science.gov (United States)

Sakurai, Nozomu; Ara, Takeshi; Enomoto, Mitsuo; Motegi, Takeshi; Morishita, Yoshihiko; Kurabayashi, Atsushi; Iijima, Yoko; Ogata, Yoshiyuki; Nakajima, Daisuke; Suzuki, Hideyuki; Shibata, Daisuke

2014-01-01

A metabolome--the collection of comprehensive quantitative data on metabolites in an organism--has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.

E-Cigarette Affects the Metabolome of Primary Normal Human Bronchial Epithelial Cells.

Science.gov (United States)

Aug, Argo; Altraja, Siiri; Kilk, Kalle; Porosk, Rando; Soomets, Ursel; Altraja, Alan

2015-01-01

E-cigarettes are widely believed to be safer than conventional cigarettes and have been even suggested as aids for smoking cessation. However, while reasonable with some regards, this judgment is not yet supported by adequate biomedical research data. Since bronchial epithelial cells are the immediate target of inhaled toxicants, we hypothesized that exposure to e-cigarettes may affect the metabolome of human bronchial epithelial cells (HBEC) and that the changes are, at least in part, induced by oxidant-driven mechanisms. Therefore, we evaluated the effect of e-cigarette liquid (ECL) on the metabolome of HBEC and examined the potency of antioxidants to protect the cells. We assessed the changes of the intracellular metabolome upon treatment with ECL in comparison of the effect of cigarette smoke condensate (CSC) with mass spectrometry and principal component analysis on air-liquid interface model of normal HBEC. Thereafter, we evaluated the capability of the novel antioxidant tetrapeptide O-methyl-l-tyrosinyl-γ-l-glutamyl-l-cysteinylglycine (UPF1) to attenuate the effect of ECL. ECL caused a significant shift in the metabolome that gradually gained its maximum by the 5th hour and receded by the 7th hour. A second alteration followed at the 13th hour. Treatment with CSC caused a significant initial shift already by the 1st hour. ECL, but not CSC, significantly increased the concentrations of arginine, histidine, and xanthine. ECL, in parallel with CSC, increased the content of adenosine diphosphate and decreased that of three lipid species from the phosphatidylcholine family. UPF1 partially counteracted the ECL-induced deviations, UPF1's maximum effect occurred at the 5th hour. The data support our hypothesis that ECL profoundly alters the metabolome of HBEC in a manner, which is comparable and partially overlapping with the effect of CSC. Hence, our results do not support the concept of harmlessness of e-cigarettes.

Formic acid hydrolysis/liquid chromatography isotope dilution mass spectrometry: An accurate method for large DNA quantification.

Science.gov (United States)

Shibayama, Sachie; Fujii, Shin-Ichiro; Inagaki, Kazumi; Yamazaki, Taichi; Takatsu, Akiko

2016-10-14

Liquid chromatography-isotope dilution mass spectrometry (LC-IDMS) with formic acid hydrolysis was established for the accurate quantification of λDNA. The over-decomposition of nucleobases in formic acid hydrolysis was restricted by optimizing the reaction temperature and the reaction time, and accurately corrected by using deoxynucleotides (dNMPs) and isotope-labeled dNMPs as the calibrator and the internal standard, respectively. The present method could quantify λDNA with an expanded uncertainty of 4.6% using 10fmol of λDNA. The analytical results obtained with the present method were validated by comparing with the results of phosphate-base quantification by inductively coupled plasma-mass spectrometry (ICP-MS). The results showed good agreement with each other. We conclude that the formic acid hydrolysis/LC-IDMS method can quantify λDNA accurately and is promising as the primary method for the certification of DNA as reference material. Copyright © 2016 Elsevier B.V. All rights reserved.

Systematic Applications of Metabolomics in Metabolic Engineering

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Robert A. Dromms

2012-12-01

Full Text Available The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Tools for the functional interpretation of metabolomic experiments.

Science.gov (United States)

Chagoyen, Monica; Pazos, Florencio

2013-11-01

The so-called 'omics' approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the 'omics' family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other 'omics', such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future.

Efficient mining of myxobacterial metabolite profiles enabled by liquid chromatography-electrospray ionisation-time-of-flight mass spectrometry and compound-based principal component analysis

International Nuclear Information System (INIS)

Krug, Daniel; Zurek, Gabriela; Schneider, Birgit; Garcia, Ronald; Mueller, Rolf

2008-01-01

Bacteria producing secondary metabolites are an important source of natural products with highly diverse structures and biological activities. Developing methods to efficiently mine procaryotic secondary metabolomes for the presence of potentially novel natural products is therefore of considerable interest. Modern mass spectrometry-coupled liquid chromatography can effectively capture microbial metabolic diversity with ever improving sensitivity and accuracy. In addition, computational and statistical tools increasingly enable the targeted analysis and exploration of information-rich LC-MS datasets. In this article, we describe the use of such techniques for the characterization of myxobacterial secondary metabolomes. Using accurate mass data from high-resolution ESI-TOF measurements, target screening has facilitated the rapid identification of known myxobacterial metabolites in extracts from nine Myxococcus species. Furthermore, principal component analysis (PCA), implementing an advanced compound-based bucketing approach, readily revealed the presence of further compounds which contribute to variation among the metabolite profiles under investigation. The generation of molecular formulae for putative novel compounds with high confidence due to evaluation of both exact mass position and isotopic pattern, is exemplified as an important key for de-replication and prioritization of candidates for further characterization

Metabolomic Effects of Xylitol and Fluoride on Plaque Biofilm in Vivo

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Takahashi, N.; Washio, J.

2011-01-01

Dental caries is initiated by demineralization of the tooth surface through acid production from sugar by plaque biofilm. Fluoride and xylitol have been used worldwide as caries-preventive reagents, based on in vitro-proven inhibitory mechanisms on bacterial acid production. We attempted to confirm the inhibitory mechanisms of fluoride and xylitol in vivo by performing metabolome analysis on the central carbon metabolism in supragingival plaque using the combination of capillary electrophoresis and a time-of-flight mass spectrometer. Fluoride (225 and 900 ppm F−) inhibited lactate production from 10% glucose by 34% and 46%, respectively, along with the increase in 3-phosphoglycerate and the decrease in phosphoenolpyruvate in the EMP pathway in supragingival plaque. These results confirmed that fluoride inhibited bacterial enolase in the EMP pathway and subsequently repressed acid production in vivo. In contrast, 10% xylitol had no effect on acid production and the metabolome profile in supragingival plaque, although xylitol 5-phosphate was produced. These results suggest that xylitol is not an inhibitor of plaque acid production but rather a non-fermentative sugar alcohol. Metabolome analyses of plaque biofilm can be applied for monitoring the efficacy of dietary components and medicines for plaque biofilm, leading to the development of effective plaque control. PMID:21940519

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

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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.

Metabolomic effects of xylitol and fluoride on plaque biofilm in vivo.

Science.gov (United States)

Takahashi, N; Washio, J

2011-12-01

Dental caries is initiated by demineralization of the tooth surface through acid production from sugar by plaque biofilm. Fluoride and xylitol have been used worldwide as caries-preventive reagents, based on in vitro-proven inhibitory mechanisms on bacterial acid production. We attempted to confirm the inhibitory mechanisms of fluoride and xylitol in vivo by performing metabolome analysis on the central carbon metabolism in supragingival plaque using the combination of capillary electrophoresis and a time-of-flight mass spectrometer. Fluoride (225 and 900 ppm F(-)) inhibited lactate production from 10% glucose by 34% and 46%, respectively, along with the increase in 3-phosphoglycerate and the decrease in phosphoenolpyruvate in the EMP pathway in supragingival plaque. These results confirmed that fluoride inhibited bacterial enolase in the EMP pathway and subsequently repressed acid production in vivo. In contrast, 10% xylitol had no effect on acid production and the metabolome profile in supragingival plaque, although xylitol 5-phosphate was produced. These results suggest that xylitol is not an inhibitor of plaque acid production but rather a non-fermentative sugar alcohol. Metabolome analyses of plaque biofilm can be applied for monitoring the efficacy of dietary components and medicines for plaque biofilm, leading to the development of effective plaque control.

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

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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.

Metabolomics as a promising tool for early osteoarthritis diagnosis

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E.B. de Sousa

2017-09-01

Full Text Available Osteoarthritis (OA is the main cause of disability worldwide, due to progressive articular cartilage loss and degeneration. According to recent research, OA is more than just a degenerative disease due to some metabolic components associated to its pathogenesis. However, no biomarker has been identified to detect this disease at early stages or to track its development. Metabolomics is an emerging field and has the potential to detect many metabolites in a single spectrum using high resolution nuclear magnetic resonance (NMR techniques or mass spectrometry (MS. NMR is a reproducible and reliable non-destructive analytical method. On the other hand, MS has a lower detection limit and is more destructive, but it is more sensitive. NMR and MS are useful for biological fluids, such as urine, blood plasma, serum, or synovial fluid, and have been used for metabolic profiling in dogs, mice, sheep, and humans. Thus, many metabolites have been listed as possibly associated to OA pathogenesis. The goal of this review is to provide an overview of the studies in animal models and humans, regarding the use of metabolomics as a tool for early osteoarthritis diagnosis. The concept of osteoarthritis as a metabolic disease and the importance of detecting a biomarker for its early diagnosis are highlighted. Then, some studies in plasma and synovial tissues are shown, and finally the application of metabolomics in the evaluation of synovial fluid is described.

QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.

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Simader, Alexandra Maria; Kluger, Bernhard; Neumann, Nora Katharina Nicole; Bueschl, Christoph; Lemmens, Marc; Lirk, Gerald; Krska, Rudolf; Schuhmacher, Rainer

2015-10-24

Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple

A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

OpenAIRE

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

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 ...

Biomarker Discovery Using New Metabolomics Software for Automated Processing of High Resolution LC-MS Data

Science.gov (United States)

Hnatyshyn, S.; Reily, M.; Shipkova, P.; McClure, T.; Sanders, M.; Peake, D.

2011-01-01

Robust biomarkers of target engagement and efficacy are required in different stages of drug discovery. Liquid chromatography coupled to high resolution mass spectrometry provides sensitivity, accuracy and wide dynamic range required for identification of endogenous metabolites in biological matrices. LCMS is widely-used tool for biomarker identification and validation. Typical high resolution LCMS profiles from biological samples may contain greater than a million mass spectral peaks corresponding to several thousand endogenous metabolites. Reduction of the total number of peaks, component identification and statistical comparison across sample groups remains to be a difficult and time consuming challenge. Blood samples from four groups of rats (male vs. female, fully satiated and food deprived) were analyzed using high resolution accurate mass (HRAM) LCMS. All samples were separated using a 15 minute reversed-phase C18 LC gradient and analyzed in both positive and negative ion modes. Data was acquired using 15K resolution and 5ppm mass measurement accuracy. The entire data set was analyzed using software developed in collaboration between Bristol Meyers Squibb and Thermo Fisher Scientific to determine the metabolic effects of food deprivation on rats. Metabolomic LC-MS data files are extraordinarily complex and appropriate reduction of the number of spectral peaks via identification of related peaks and background removal is essential. A single component such as hippuric acid generates more than 20 related peaks including isotopic clusters, adducts and dimers. Plasma and urine may contain 500-1500 unique quantifiable metabolites. Noise filtering approaches including blank subtraction were used to reduce the number of irrelevant peaks. By grouping related signals such as isotopic peaks and alkali adducts, data processing was greatly simplified by reducing the total number of components by 10-fold. The software processes 48 samples in under 60minutes. Principle

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

Science.gov (United States)

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.

Early Metabolome Profiling and Prognostic Value in Paraquat-Poisoned Patients: Based on Ultraperformance Liquid Chromatography Coupled To Quadrupole Time-of-Flight Mass Spectrometry.

Science.gov (United States)

Hu, Lufeng; Hong, Guangliang; Tang, Yahui; Wang, Xianqin; Wen, Congcong; Lin, Feiyan; Lu, Zhongqiu

2017-12-18

Paraquat (PQ) has caused countless deaths throughout the world. There remains no effective treatment for PQ poisoning. Here we study the blood metabolome of PQ-poisoned patients using ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). Patients were divided into groups according to blood PQ concentration. Healthy subjects served as controls. Metabolic features were statistically analyzed using multivariate pattern-recognition techniques to identify the most important metabolites. Selected metabolites were further compared with a series of clinical indexes to assess the prognostic value. PQ-poisoned patients showed substantial differences compared with healthy subjects. Based on variable of importance in the project (VIP) values and statistical analysis, 17 metabolites were selected and identified. These metabolites well-classified low PQ-poisoned patients, high PQ-poisoned patients, and healthy subjects, which was better than that of a complete blood count (CBC). Among the 17 metabolites, 20:3/18:1-PC (PC), LPA (0:0/16:0) (LPA), 19-oxo-deoxycorticosterone (19-oxo-DOC), and eicosapentaenoic acid (EPA) had prognostic value. In particular, EPA was the most sensitive one. Besides, the levels of EPA was correlated with LPA and 19-oxo-DOC. If EPA was excessively consumed, then prognosis was poor. In conclusion, the serum metabolome is substantially perturbed by PQ poisoning. EPA is the most important biomarker in early PQ poisoning.

NMR-based metabolomics reveals urinary metabolome modifications in female Sprague-Dawley rats by cranberry procyanidins.

Science.gov (United States)

Liu, Haiyan; Tayyari, Fariba; Edison, Arthur S; Su, Zhihua; Gu, Liwei

2016-08-01

A (1)H NMR global metabolomics approach was used to investigate the urinary metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) or partially purified apple procyanidins (PPAP). After collecting 24-h baseline urine, 24 female Sprague-Dawley rats were randomly separated into two groups and gavaged with PPCP or PPAP twice using a dose of 250 mg extracts per kilogram body weight. The 24-h urine samples were collected after the gavage. Urine samples were analyzed using (1)H NMR. Multivariate analyses showed that the urinary metabolome in rats was modified after administering PPCP or PPAP compared to baseline urine metabolic profiles. 2D (1)H-(13)C HSQC NMR was conducted to assist identification of discriminant metabolites. An increase of hippurate, lactate and succinate and a decrease of citrate and α-ketoglutarate were observed in rat urine after administering PPCP. Urinary levels of d-glucose, d-maltose, 3-(3'-hydroxyphenyl)-3-hydroxypropanoic acid, p-hydroxyphenylacetic acid, formate and phenol increased but citrate, α-ketoglutarate and creatinine decreased in rats after administering PPAP. Furthermore, the NMR analysis showed that the metabolome in the urine of rats administered with PPCP differed from those gavaged with PPAP. Compared to PPAP, PPCP caused an increase of urinary excretion of hippurate but a decrease of 3-(3'-hydroxyphenyl)-3-hydroxypropanoic acid, p-hydroxyphenylacetic acid and phenol. These metabolome changes caused by cranberry procyanidins may help to explain its reported health benefits and identify biomarkers of cranberry procyanidin intake. Copyright © 2016 Elsevier Inc. All rights reserved.

NMR-based metabolomic profiling of overweight adolescents

DEFF Research Database (Denmark)

Zheng, Hong; Yde, Christian C; Arnberg, Karina

2014-01-01

The plasma and urine metabolome of 192 overweight 12-15-year-old adolescents (BMI of 25.4 ± 2.3 kg/m(2)) were examined in order to elucidate gender, pubertal development measured as Tanner stage, physical activity measured as number of steps taken daily, and intra-/interindividual differences...... and the metabolome could be identified. The present study for the first time provides comprehensive information about associations between the metabolome and gender, pubertal development, and physical activity in overweight adolescents, which is an important subject group to approach in the prevention of obesity...... affecting the metabolome detected by proton NMR spectroscopy. Higher urinary excretion of citrate, creatinine, hippurate, and phenylacetylglutamine and higher plasma level of phosphatidylcholine and unsaturated lipid were found for girls compared with boys. The results suggest that gender differences...

Metabolomic Analysis of Two Parmotrema Lichens: P. robustum (Degel. Hale and P. andinum (Mull. Arg. Hale Using UHPLC-ESI-OT-MS-MS

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Alfredo Torres-Benítez

2017-10-01

Full Text Available Lichens are symbiotic associations of fungi with microalgae and/or cyanobacteria. Lichens belonging to the Parmeliaceae family comprise 2700 species of lichens, including the Parmotrema genus which is composed of 300 species. The metabolites of this genus include depsides, depsidones, phenolics, polysaccharides, lipids, diphenylethers and dibenzofurans, which are responsible for the biological activities reported including antidiabetic, antihelmintic, anticancer, antioxidant, antibacterial, anti-inflammatory, antimitotic, antitumoral, antifungal, and antioxidant enzyme inhibitory. Due to scarce knowledge of metabolomic profiles of Parmotrema species (P. andinum and P. robustum, a full metabolome study based on ultra-high performance liquid chromatography- diode array detector-electrospray ionization-quadrupole-orbitrap-mass-spectrometry (UHPLC-DAD-ESI-Q-orbitrap MS was performed for a comprehensive characterization of their substances. From the methanolic extracts of these species, a total of 54 metabolites were identified for the first time using this hyphenated technique, including thirty compounds in P. andinum, and thirty-seven in P. robustum. Moreover, two compounds were not identified as known compounds, and could be new structures, according to our data. This report shows that this technique is effective and accurate for rapid chemical identification of lichen substances and the compounds identified could serve as chemotaxonomic markers to differentiate these ruffle lichens.

The food metabolome: a window over dietary exposure.

Science.gov (United States)

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.

Evaluation of analytical performance and reliability of direct nanoLC-nanoESI-high resolution mass spectrometry for profiling the (xeno)metabolome.

Science.gov (United States)

Chetwynd, Andrew J; David, Arthur; Hill, Elizabeth M; Abdul-Sada, Alaa

2014-10-01

Mass spectrometry (MS) profiling techniques are used for analysing metabolites and xenobiotics in biofluids; however, detection of low abundance compounds using conventional MS techniques is poor. To counter this, nanoflow ultra-high-pressure liquid chromatography-nanoelectrospray ionization-time-of-flight MS (nUHPLC-nESI-TOFMS), which has been used primarily for proteomics, offers an innovative prospect for profiling small molecules. Compared to conventional UHPLC-ESI-TOFMS, nUHPLC-nESI-TOFMS enhanced detection limits of a variety of (xeno)metabolites by between 2 and 2000-fold. In addition, this study demonstrates for the first time excellent repeatability and reproducibility for analysis of urine and plasma samples using nUHPLC-nESI-TOFMS, supporting implementation of this platform as a novel approach for high-throughput (xeno)metabolomics. Copyright © 2014 John Wiley & Sons, Ltd.

Metabolomics in cancer biomarker discovery: current trends and future perspectives.

Science.gov (United States)

Armitage, Emily G; Barbas, Coral

2014-01-01

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted. Copyright © 2013 Elsevier B.V. All rights reserved.

Phenotyping of Chronic Obstructive Pulmonary Disease Based on the Integration of Metabolomes and Clinical Characteristics

Directory of Open Access Journals (Sweden)

Kalle Kilk

2018-02-01

Full Text Available Apart from the refined management-oriented clinical stratification of chronic obstructive pulmonary disease (COPD, the molecular pathologies behind this highly prevalent disease have remained obscure. The aim of this study was the characterization of patients with COPD, based on the metabolomic profiling of peripheral blood and exhaled breath condensate (EBC within the context of defined clinical and demographic variables. Mass-spectrometry-based targeted analysis of serum metabolites (mainly amino acids and lipid species, untargeted profiles of serum and EBC of patients with COPD of different clinical characteristics (n = 25 and control individuals (n = 21 were performed. From the combined clinical/demographic and metabolomics data, associations between clinical/demographic and metabolic parameters were searched and a de novo phenotyping for COPD was attempted. Adjoining the clinical parameters, sphingomyelins were the best to differentiate COPD patients from controls. Unsaturated fatty acid-containing lipids, ornithine metabolism and plasma protein composition-associated signals from the untargeted analysis differentiated the Global Initiative for COPD (GOLD categories. Hierarchical clustering did not reveal a clinical-metabolomic stratification superior to the strata set by the GOLD consensus. We conclude that while metabolomics approaches are good for finding biomarkers and clarifying the mechanism of the disease, there are no distinct co-variate independent clinical-metabolic phenotypes.

Gut metabolome meets microbiome

DEFF Research Database (Denmark)

Lamichhane, Santosh; Sen, Partho; Dickens, Alex M

2018-01-01

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...

Metabolomic profiles as reliable biomarkers of dietary composition123

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Esko, Tõnu; Hirschhorn, Joel N; Feldman, Henry A; Hsu, Yu-Han H; Deik, Amy A; Clish, Clary B; Ebbeling, Cara B; Ludwig, David S

2017-01-01

Background: Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects. Objective: We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol. Design: Twenty-one adults with a high body mass index (in kg/m2; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10–15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography–tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons. Results: Of 333 metabolites, we identified 152 whose concentrations differed for ≥1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors. Conclusions: Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in

Fenofibrate Metabolism in the Cynomolgus Monkey using Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry-Based MetabolomicsS⃞

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Liu, Aiming; Patterson, Andrew D.; Yang, Zongtao; Zhang, Xinying; Liu, Wei; Qiu, Fayang; Sun, He; Krausz, Kristopher W.; Idle, Jeffrey R.; Gonzalez, Frank J.; Dai, Renke

2009-01-01

Fenofibrate, widely used for the treatment of dyslipidemia, activates the nuclear receptor, peroxisome proliferator-activated receptor α. However, liver toxicity, including liver cancer, occurs in rodents treated with fibrate drugs. Marked species differences occur in response to fibrate drugs, especially between rodents and humans, the latter of which are resistant to fibrate-induced cancer. Fenofibrate metabolism, which also shows species differences, has not been fully determined in humans and surrogate primates. In the present study, the metabolism of fenofibrate was investigated in cynomolgus monkeys by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS)-based metabolomics. Urine samples were collected before and after oral doses of fenofibrate. The samples were analyzed in both positive-ion and negative-ion modes by UPLC-QTOFMS, and after data deconvolution, the resulting data matrices were subjected to multivariate data analysis. Pattern recognition was performed on the retention time, mass/charge ratio, and other metabolite-related variables. Synthesized or purchased authentic compounds were used for metabolite identification and structure elucidation by liquid chromatographytandem mass spectrometry. Several metabolites were identified, including fenofibric acid, reduced fenofibric acid, fenofibric acid ester glucuronide, reduced fenofibric acid ester glucuronide, and compound X. Another two metabolites (compound B and compound AR), not previously reported in other species, were characterized in cynomolgus monkeys. More importantly, previously unknown metabolites, fenofibric acid taurine conjugate and reduced fenofibric acid taurine conjugate were identified, revealing a previously unrecognized conjugation pathway for fenofibrate. PMID:19251819

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

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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

Cerebrospinal Fluid Metabolomics After Natural Product Treatment in an Experimental Model of Cerebral Ischemia.

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Huan, Tao; Xian, Jia Wen; Leung, Wing Nang; Li, Liang; Chan, Chun Wai

2016-11-01

Cerebrospinal fluid (CSF) is an important biofluid for diagnosis of and research on neurological diseases. However, in-depth metabolomic profiling of CSF remains an analytical challenge due to the small volume of samples, particularly in small animal models. In this work, we report the application of a high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) workflow for CSF metabolomics in Gastrodia elata and Uncaria rhynchophylla water extract (GUW)-treated experimental cerebral ischemia model of rat. The GUW is a commonly used Traditional Chinese Medicine (TCM) for hypertension and brain disease. This study investigated the amine- and phenol-containing biomarkers in the CSF metabolome. After GUW treatment for 7 days, the neurological deficit score was significantly improved with infarct volume reduction, while the integrity of brain histological structure was preserved. Over 1957 metabolites were quantified in CSF by dansylation LC-MS. The analysis of this comprehensive list of metabolites suggests that metabolites associated with oxidative stress, inflammatory response, and excitotoxicity change during GUW-induced alleviation of ischemic injury. This work is significant in that (1) it shows CIL LC-MS can be used for in-depth profiling of the CSF metabolome in experimental ischemic stroke and (2) identifies several potential molecular targets (that might mediate the central nervous system) and associate with pharmacodynamic effects of some frequently used TCMs.

Protective effect of natural products and hormones in colon cancer using metabolome: A physiological overview

Directory of Open Access Journals (Sweden)

Khaled Mohamed Mohamed Koriem

2017-10-01

Full Text Available Globally, the third cause of males cancer and the fourth cause of females cancer is colon cancer (CC. In Egypt, high CC percentage occurs in children and in individuals below 40 years of age. The complete loss of biological enzyme function is the main cause of CC and consequently CC increased in smoking and pollution exposure. The aim of this review is to focus on the application of metabolome as a physiological tool that can play an important role in preventing CC incidence by natural products and hormones. The dietary factors, intestinal micro-flora and endogenously produced metabolites are the main three causes that produce free radicals in the colon. A correlation occurs between the enzyme activity and CC polymorphisms or property. Nowadays metabolome is applied with the progress of different analytical methods, data bases and tools for cancer predication and stimulation especially in CC cases. Metabolism is defined as intracellular chemical reactions that produce chemical substances and energies sustaining life. Metabolic pathway networks are also composed of links that are defined as transformation of chemical structures between two metabolites and an enzyme reaction. The most important advantage of metabolome is its ability to analyze metabolites from any source, regardless of origin, where the application of liquid chromatography combined with mass spectra in metabolome analysis to a series of cancer cell lines that were progressively more tumorigenic due to the induction of 1,2,3 or 4 oncogenes to cell lines could be a metabolome example application. In conclusion, natural products and hormones are very important in preventing CC in humans and animal models where both natural products and hormones play a significant and important effect in regulating physiological process especially in CC cases. In this situation, metabolome must increase in its application in the future for the diagnosis of CC cases.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

International Nuclear Information System (INIS)

Mercier, Pascal; Lewis, Michael J.; Chang, David; Baker, David; Wishart, David S.

2011-01-01

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or “quantitative” metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Energy Technology Data Exchange (ETDEWEB)

Mercier, Pascal; Lewis, Michael J.; Chang, David, E-mail: dchang@chenomx.com [Chenomx Inc (Canada); Baker, David [Pfizer Inc (United States); Wishart, David S. [University of Alberta, Department of Computing Science and Biological Sciences (Canada)

2011-04-15

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or 'quantitative' metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Compliance with minimum information guidelines in public metabolomics repositories.

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Spicer, Rachel A; Salek, Reza; Steinbeck, Christoph

2017-09-26

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards.

Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats.

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Rivera-Vélez, Sol-Maiam; Villarino, Nicolas F

2018-02-01

Objectives This aim of this study was to characterize the composition and content of the feline urine metabolome. Methods Eight healthy domestic cats were acclimated at least 10 days before starting the study. Urine samples (~2 ml) were collected by ultrasound-guided cystocentesis. Samples were centrifuged at 1000 × g for 8 mins, and the supernatant was analyzed by gas chromatography/time-of-flight mass spectrometery. The urine metabolome was characterized using an untargeted metabolomics approach. Results Three hundred and eighteen metabolites were detected in the urine of the eight cats. These molecules are key components of at least 100 metabolic pathways. Feline urine appears to be dominated by carbohydrates, carbohydrate conjugates, organic acid and derivatives, and amino acids and analogs. The five most abundant molecules were phenaceturic acid, hippuric acid, pseudouridine phosphate and 3-(4-hydroxyphenyl) propionic acid. Conclusions and relevance This study is the first to characterize the feline urine metabolome. The results of this study revealed the presence of multiple low-molecular-weight substances that were not known to be present in feline urine. As expected, the origin of the metabolites detected in urine was diverse, including endogenous compounds and molecules biosynthesized by microbes. Also, the diet seemed to have had a relevant role on the urine metabolome. Further exploration of the urine metabolic phenotype will open a window for discovering unknown, or poorly understood, metabolic pathways. In turn, this will advance our understanding of feline biology and lead to new insights in feline physiology, nutrition and medicine.

Screening and confirmation criteria for hormone residue analysis using liquid chromatography accurate mass time-of-flight, Fourier transform ion cyclotron resonance and orbitrap mass spectrometry techniques

NARCIS (Netherlands)

Nielen, M.W.F.; Engelen, M.C. van; Zuiderent, R.; Ramaker, R.

2007-01-01

An emerging trend is recognised in hormone and veterinary drug residue analysis from liquid chromatography tandem mass spectrometry (LC/MS/MS) based screening and confirmation towards accurate mass alternatives such as LC coupled with time-of-flight (TOF), Fourier transform ion cyclotron resonance

Ultrasound: a subexploited tool for sample preparation in metabolomics.

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Luque de Castro, M D; Delgado-Povedano, M M

2014-01-02

Metabolomics, one of the most recently emerged "omics", has taken advantage of ultrasound (US) to improve sample preparation (SP) steps. The metabolomics-US assisted SP step binomial has experienced a dissimilar development that has depended on the area (vegetal or animal) and the SP step. Thus, vegetal metabolomics and US assisted leaching has received the greater attention (encompassing subdisciplines such as metallomics, xenometabolomics and, mainly, lipidomics), but also liquid-liquid extraction and (bio)chemical reactions in metabolomics have taken advantage of US energy. Also clinical and animal samples have benefited from US assisted SP in metabolomics studies but in a lesser extension. The main effects of US have been shortening of the time required for the given step, and/or increase of its efficiency or availability for automation; nevertheless, attention paid to potential degradation caused by US has been scant or nil. Achievements and weak points of the metabolomics-US assisted SP step binomial are discussed and possible solutions to the present shortcomings are exposed. Copyright © 2013 Elsevier B.V. All rights reserved.

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

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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.

IsoMS: automated processing of LC-MS data generated by a chemical isotope labeling metabolomics platform.

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Zhou, Ruokun; Tseng, Chiao-Li; Huan, Tao; Li, Liang

2014-05-20

A chemical isotope labeling or isotope coded derivatization (ICD) metabolomics platform uses a chemical derivatization method to introduce a mass tag to all of the metabolites having a common functional group (e.g., amine), followed by LC-MS analysis of the labeled metabolites. To apply this platform to metabolomics studies involving quantitative analysis of different groups of samples, automated data processing is required. Herein, we report a data processing method based on the use of a mass spectral feature unique to the chemical labeling approach, i.e., any differential-isotope-labeled metabolites are detected as peak pairs with a fixed mass difference in a mass spectrum. A software tool, IsoMS, has been developed to process the raw data generated from one or multiple LC-MS runs by peak picking, peak pairing, peak-pair filtering, and peak-pair intensity ratio calculation. The same peak pairs detected from multiple samples are then aligned to produce a CSV file that contains the metabolite information and peak ratios relative to a control (e.g., a pooled sample). This file can be readily exported for further data and statistical analysis, which is illustrated in an example of comparing the metabolomes of human urine samples collected before and after drinking coffee. To demonstrate that this method is reliable for data processing, five (13)C2-/(12)C2-dansyl labeled metabolite standards were analyzed by LC-MS. IsoMS was able to detect these metabolites correctly. In addition, in the analysis of a (13)C2-/(12)C2-dansyl labeled human urine, IsoMS detected 2044 peak pairs, and manual inspection of these peak pairs found 90 false peak pairs, representing a false positive rate of 4.4%. IsoMS for Windows running R is freely available for noncommercial use from www.mycompoundid.org/IsoMS.

Proteomic and metabolomic approaches to biomarker discovery

CERN Document Server

Issaq, Haleem J

2013-01-01

Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution.  The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis...

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

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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.

Effect of masticatory stimulation on the quantity and quality of saliva and the salivary metabolomic profile.

Directory of Open Access Journals (Sweden)

Nobuyuki Okuma

Full Text Available This study characterized the changes in quality and quantity of saliva, and changes in the salivary metabolomic profile, to understand the effects of masticatory stimulation.Stimulated and unstimulated saliva samples were collected from 55 subjects and salivary hydrophilic metabolites were comprehensively quantified using capillary electrophoresis-time-of-flight mass spectrometry.In total, 137 metabolites were identified and quantified. The concentrations of 44 metabolites in stimulated saliva were significantly higher than those in unstimulated saliva. Pathway analysis identified the upregulation of the urea cycle and synthesis and degradation pathways of glycine, serine, cysteine and threonine in stimulated saliva. A principal component analysis revealed that the effect of masticatory stimulation on salivary metabolomic profiles was less dependent on sample population sex, age, and smoking. The concentrations of only 1 metabolite in unstimulated saliva, and of 3 metabolites stimulated saliva, showed significant correlation with salivary secretion volume, indicating that the salivary metabolomic profile and salivary secretion volume were independent factors.Masticatory stimulation affected not only salivary secretion volume, but also metabolite concentration patterns. A low correlation between the secretion volume and these patterns supports the conclusion that the salivary metabolomic profile may be a new indicator to characterize masticatory stimulation.

Serum metabolomics differentiating pancreatic cancer from new-onset diabetes

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He, Xiangyi; Zhong, Jie; Wang, Shuwei; Zhou, Yufen; Wang, Lei; Zhang, Yongping; Yuan, Yaozong

2017-01-01

To establish a screening strategy for pancreatic cancer (PC) based on new-onset diabetic mellitus (NO-DM), serum metabolomics analysis and a search for the metabolic pathways associated with PC related DM were performed. Serum samples from patients with NO-DM (n = 30) and patients with pancreatic cancer and NO-DM were examined by liquid chromatography-mass spectrometry. Data were analyzed using principal components analysis (PCA) and orthogonal projection to latent structures (OPLS) of the most significant metabolites. The diagnostic model was constructed using logistic regression analysis. Metabolic pathways were analyzed using the web-based tool MetPA. PC patients with NO-DM were older and had a lower BMI and shorter duration of DM than those with NO-DM. The metabolomic profiles of patients with PC and NO-DM were significantly different from those of patients with NO-DM in the PCA and OPLS models. Sixty two differential metabolites were identified by the OPLS model. The logistic regression model using a panel of two metabolites including N_Succinyl_L_diaminopimelic_acid and PE (18:2) had high sensitivity (93.3%) and specificity (93.1%) for PC. The top three metabolic pathways associated with PC related DM were valine, leucine and isoleucine biosynthesis and degradation, primary bile acid biosynthesis, and sphingolipid metabolism. In conclusion, screening for PC based on NO-DM using serum metabolomics in combination with clinic characteristics and CA19-9 is a potential useful strategy. Several metabolic pathways differed between PC related DM and type 2 DM. PMID:28418859

An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values.

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Sakurai, Nozomu; Ara, Takeshi; Kanaya, Shigehiko; Nakamura, Yukiko; Iijima, Yoko; Enomoto, Mitsuo; Motegi, Takeshi; Aoki, Koh; Suzuki, Hideyuki; Shibata, Daisuke

2013-01-15

High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service 'MFSearcher' to query the database in a RESTful manner. Available for free at http://webs2.kazusa.or.jp/mfsearcher. The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. sakurai@kazusa.or.jp Supplementary data are available at Bioinformatics online.

Metabolomics for functional genomics, systems biology, and biotechnology.

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Saito, Kazuki; Matsuda, Fumio

2010-01-01

Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. Plants collectively produce a huge array of chemicals, far more than are produced by most other organisms; hence, metabolomics is of great importance in plant biology. Although substantial improvements have been made in the field of metabolomics, the uniform annotation of metabolite signals in databases and informatics through international standardization efforts remains a challenge, as does the development of new fields such as fluxome analysis and single cell analysis. The principle of transcript and metabolite cooccurrence, particularly transcriptome coexpression network analysis, is a powerful tool for decoding the function of genes in Arabidopsis thaliana. This strategy can now be used for the identification of genes involved in specific pathways in crops and medicinal plants. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis, prediction of food quality, and evaluation of genetically modified crops. Systems biology driven by metabolome data will aid in deciphering the secrets of plant cell systems and their application to biotechnology.

Fecal Microbiota and Metabolome in a Mouse Model of Spontaneous Chronic Colitis: Relevance to Human Inflammatory Bowel Disease.

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Robinson, Ainsley M; Gondalia, Shakuntla V; Karpe, Avinash V; Eri, Rajaraman; Beale, David J; Morrison, Paul D; Palombo, Enzo A; Nurgali, Kulmira

2016-12-01

Dysbiosis of the gut microbiota may be involved in the pathogenesis of inflammatory bowel disease (IBD). However, the mechanisms underlying the role of the intestinal microbiome and metabolome in IBD onset and its alteration during active treatment and recovery remain unknown. Animal models of chronic intestinal inflammation with similar microbial and metabolomic profiles would enable investigation of these mechanisms and development of more effective treatments. Recently, the Winnie mouse model of colitis closely representing the clinical symptoms and characteristics of human IBD has been developed. In this study, we have analyzed fecal microbial and metabolomic profiles in Winnie mice and discussed their relevance to human IBD. The 16S rRNA gene was sequenced from fecal DNA of Winnie and C57BL/6 mice to define operational taxonomic units at ≥97% similarity threshold. Metabolomic profiling of the same fecal samples was performed by gas chromatography-mass spectrometry. Composition of the dominant microbiota was disturbed, and prominent differences were evident at all levels of the intestinal microbiome in fecal samples from Winnie mice, similar to observations in patients with IBD. Metabolomic profiling revealed that chronic colitis in Winnie mice upregulated production of metabolites and altered several metabolic pathways, mostly affecting amino acid synthesis and breakdown of monosaccharides to short chain fatty acids. Significant dysbiosis in the Winnie mouse gut replicates many changes observed in patients with IBD. These results provide justification for the suitability of this model to investigate mechanisms underlying the role of intestinal microbiota and metabolome in the pathophysiology of IBD.

Behavioral metabolomics analysis identifies novel neurochemical signatures in methamphetamine sensitization

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Adkins, Daniel E.; McClay, Joseph L.; Vunck, Sarah A.; Batman, Angela M.; Vann, Robert E.; Clark, Shaunna L.; Souza, Renan P.; Crowley, James J.; Sullivan, Patrick F.; van den Oord, Edwin J.C.G.; Beardsley, Patrick M.

2014-01-01

Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In the present study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate < 0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent methamphetamine levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization. PMID:24034544

Accurate mass analysis of ethanesulfonic acid degradates of acetochlor and alachlor using high-performance liquid chromatography and time-of-flight mass spectrometry

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Thurman, E.M.; Ferrer, I.; Parry, R.

2002-01-01

Degradates of acetochlor and alachlor (ethanesulfonic acids, ESAs) were analyzed in both standards and in a groundwater sample using high-performance liquid chromatography-time-of-flight mass spectrometry with electrospray ionization. The negative pseudomolecular ion of the secondary amide of acetochlor ESA and alachlor ESA gave average masses of 256.0750??0.0049 amu and 270.0786??0.0064 amu respectively. Acetochlor and alachlor ESA gave similar masses of 314.1098??0.0061 amu and 314.1153??0.0048 amu; however, they could not be distinguished by accurate mass because they have the same empirical formula. On the other hand, they may be distinguished using positive-ion electrospray because of different fragmentation spectra, which did not occur using negative-ion electrospray.

Data standards can boost metabolomics research, and if there is a will, there is a way.

Science.gov (United States)

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.

A Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease PatientsSummary

Directory of Open Access Journals (Sweden)

Jonathan P. Jacobs

2016-11-01

Full Text Available Background & Aims: Microbes may increase susceptibility to inflammatory bowel disease (IBD by producing bioactive metabolites that affect immune activity and epithelial function. We undertook a family based study to identify microbial and metabolic features of IBD that may represent a predisease risk state when found in healthy first-degree relatives. Methods: Twenty-one families with pediatric IBD were recruited, comprising 26 Crohn’s disease patients in clinical remission, 10 ulcerative colitis patients in clinical remission, and 54 healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing, untargeted liquid chromatography–mass spectrometry metabolomics, and calprotectin measurement. Individuals were grouped into microbial and metabolomics states using Dirichlet multinomial models. Multivariate models were used to identify microbes and metabolites associated with these states. Results: Individuals were classified into 2 microbial community types. One was associated with IBD but irrespective of disease status, had lower microbial diversity, and characteristic shifts in microbial composition including increased Enterobacteriaceae, consistent with dysbiosis. This microbial community type was associated similarly with IBD and reduced microbial diversity in an independent pediatric cohort. Individuals also clustered bioinformatically into 2 subsets with shared fecal metabolomics signatures. One metabotype was associated with IBD and was characterized by increased bile acids, taurine, and tryptophan. The IBD-associated microbial and metabolomics states were highly correlated, suggesting that they represented an integrated ecosystem. Healthy relatives with the IBD-associated microbial community type had an increased incidence of elevated fecal calprotectin. Conclusions: Healthy first-degree relatives can have dysbiosis associated with an altered intestinal metabolome that may signify a predisease microbial

E-Cigarette Affects the Metabolome of Primary Normal Human Bronchial Epithelial Cells.

Directory of Open Access Journals (Sweden)

Argo Aug

Full Text Available E-cigarettes are widely believed to be safer than conventional cigarettes and have been even suggested as aids for smoking cessation. However, while reasonable with some regards, this judgment is not yet supported by adequate biomedical research data. Since bronchial epithelial cells are the immediate target of inhaled toxicants, we hypothesized that exposure to e-cigarettes may affect the metabolome of human bronchial epithelial cells (HBEC and that the changes are, at least in part, induced by oxidant-driven mechanisms. Therefore, we evaluated the effect of e-cigarette liquid (ECL on the metabolome of HBEC and examined the potency of antioxidants to protect the cells. We assessed the changes of the intracellular metabolome upon treatment with ECL in comparison of the effect of cigarette smoke condensate (CSC with mass spectrometry and principal component analysis on air-liquid interface model of normal HBEC. Thereafter, we evaluated the capability of the novel antioxidant tetrapeptide O-methyl-l-tyrosinyl-γ-l-glutamyl-l-cysteinylglycine (UPF1 to attenuate the effect of ECL. ECL caused a significant shift in the metabolome that gradually gained its maximum by the 5th hour and receded by the 7th hour. A second alteration followed at the 13th hour. Treatment with CSC caused a significant initial shift already by the 1st hour. ECL, but not CSC, significantly increased the concentrations of arginine, histidine, and xanthine. ECL, in parallel with CSC, increased the content of adenosine diphosphate and decreased that of three lipid species from the phosphatidylcholine family. UPF1 partially counteracted the ECL-induced deviations, UPF1's maximum effect occurred at the 5th hour. The data support our hypothesis that ECL profoundly alters the metabolome of HBEC in a manner, which is comparable and partially overlapping with the effect of CSC. Hence, our results do not support the concept of harmlessness of e-cigarettes.

Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars.

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Lin, Hong; Rao, Jun; Shi, Jianxin; Hu, Chaoyang; Cheng, Fang; Wilson, Zoe A; Zhang, Dabing; Quan, Sheng

2014-09-01

Soybean [Glycine max (L.) Merr.] is one of the world's major crops, and soybean seeds are a rich and important resource for proteins and oils. While "omics" studies, such as genomics, transcriptomics, and proteomics, have been widely applied in soybean molecular research, fewer metabolomic studies have been conducted for large-scale detection of low molecular weight metabolites, especially in soybean seeds. In this study, we investigated the seed metabolomes of 29 common soybean cultivars through combined gas chromatography-mass spectrometry and ultra-performance liquid chromatography-tandem mass spectrometry. One hundred sixty-nine named metabolites were identified and subsequently used to construct a metabolic network of mature soybean seed. Among the 169 detected metabolites, 104 were found to be significantly variable in their levels across tested cultivars. Metabolite markers that could be used to distinguish genetically related soybean cultivars were also identified, and metabolite-metabolite correlation analysis revealed some significant associations within the same or among different metabolite groups. Findings from this work may potentially provide the basis for further studies on both soybean seed metabolism and metabolic engineering to improve soybean seed quality and yield. © 2014 Institute of Botany, Chinese Academy of Sciences.

Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars

Institute of Scientific and Technical Information of China (English)

Hong Lin; Jun Rao; Jianxin Shi; Chaoyang Hu; Fang Cheng; Zoe AWilson; Dabing Zhang; Sheng Quan

2014-01-01

Soybean [Glycine max (L.) Merr.] is one of the world’s major crops, and soybean seeds are a rich and important resource for proteins and oils. While “omics”studies, such as genomics, transcriptomics, and proteomics, have been widely applied in soybean molecular research, fewer metabolomic studies have been conducted for large-scale detection of low molecular weight metabolites, especial y in soybean seeds. In this study, we investigated the seed metabolomes of 29 common soybean cultivars through combined gas chromatography-mass spectrometry and ultra-performance liquid chromatography-tandem mass spectrometry. One hundred sixty-nine named metabolites were identified and subsequently used to construct a metabolic network of mature soybean seed. Among the 169 detected metabolites, 104 were found to be significantly variable in their levels across tested cultivars. Metabolite markers that could be used to distinguish genetical y related soybean cultivars were also identified, and metabolite-metabolite correlation analysis revealed some significant associations within the same or among different metabolite groups. Findings from this work may potentially provide the basis for further studies on both soybean seed metabolism and metabolic engineering to improve soybean seed quality and yield.

Exploring natural variation of Pinus pinaster Aiton using metabolomics: Is it possible to identify the region of origin of a pine from its metabolites?

Science.gov (United States)

Meijón, Mónica; Feito, Isabel; Oravec, Michal; Delatorre, Carolina; Weckwerth, Wolfram; Majada, Juan; Valledor, Luis

2016-02-01

Natural variation of the metabolome of Pinus pinaster was studied to improve understanding of its role in the adaptation process and phenotypic diversity. The metabolomes of needles and the apical and basal section of buds were analysed in ten provenances of P. pinaster, selected from France, Spain and Morocco, grown in a common garden for 5 years. The employment of complementary mass spectrometry techniques (GC-MS and LC-Orbitrap-MS) together with bioinformatics tools allowed the reliable quantification of 2403 molecular masses. The analysis of the metabolome showed that differences were maintained across provenances and that the metabolites characteristic of each organ are mainly related to amino acid metabolism, while provenances were distinguishable essentially through secondary metabolism when organs were analysed independently. Integrative analyses of metabolome, environmental and growth data provided a comprehensive picture of adaptation plasticity in conifers. These analyses defined two major groups of plants, distinguished by secondary metabolism: that is, either Atlantic or Mediterranean provenance. Needles were the most sensitive organ, where strong correlations were found between flavonoids and the water regime of the geographic origin of the provenance. The data obtained point to genome specialization aimed at maximizing the drought stress resistance of trees depending on their origin. © 2016 John Wiley & Sons Ltd.

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

Science.gov (United States)

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.

Enzymatically Modified Starch Ameliorates Postprandial Serum Triglycerides and Lipid Metabolome in Growing Pigs.

Science.gov (United States)

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 (Pmetabolome 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.

The application of micro-coil NMR probe technology to metabolomics of urine and serum

International Nuclear Information System (INIS)

Grimes, John H.; O’Connell, Thomas M.

2011-01-01

Increasing the sensitivity and throughput of NMR-based metabolomics is critical for the continued growth of this field. In this paper the application of micro-coil NMR probe technology was evaluated for this purpose. The most commonly used biofluids in metabolomics are urine and serum. In this study we examine different sample limited conditions and compare the detection sensitivity of the micro-coil with a standard 5 mm NMR probe. Sample concentration is evaluated as a means to leverage the greatly improved mass sensitivity of the micro-coil probes. With very small sample volumes, the sensitivity of the micro-coil probe does indeed provide a significant advantage over the standard probe. Concentrating the samples does improve the signal detection, but the benefits do not follow the expected linear increase and are both matrix and metabolite specific. Absolute quantitation will be affected by concentration, but an analysis of relative concentrations is still possible. The choice of the micro-coil probe over a standard tube based probe will depend upon a number of factors including number of samples and initial volume but this study demonstrates the feasibility of high-throughput metabolomics with the micro-probe platform.

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

Science.gov (United States)

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 Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease Patients.

Science.gov (United States)

Jacobs, Jonathan P; Goudarzi, Maryam; Singh, Namita; Tong, Maomeng; McHardy, Ian H; Ruegger, Paul; Asadourian, Miro; Moon, Bo-Hyun; Ayson, Allyson; Borneman, James; McGovern, Dermot P B; Fornace, Albert J; Braun, Jonathan; Dubinsky, Marla

2016-11-01

Microbes may increase susceptibility to inflammatory bowel disease (IBD) by producing bioactive metabolites that affect immune activity and epithelial function. We undertook a family based study to identify microbial and metabolic features of IBD that may represent a predisease risk state when found in healthy first-degree relatives. Twenty-one families with pediatric IBD were recruited, comprising 26 Crohn's disease patients in clinical remission, 10 ulcerative colitis patients in clinical remission, and 54 healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing, untargeted liquid chromatography-mass spectrometry metabolomics, and calprotectin measurement. Individuals were grouped into microbial and metabolomics states using Dirichlet multinomial models. Multivariate models were used to identify microbes and metabolites associated with these states. Individuals were classified into 2 microbial community types. One was associated with IBD but irrespective of disease status, had lower microbial diversity, and characteristic shifts in microbial composition including increased Enterobacteriaceae, consistent with dysbiosis. This microbial community type was associated similarly with IBD and reduced microbial diversity in an independent pediatric cohort. Individuals also clustered bioinformatically into 2 subsets with shared fecal metabolomics signatures. One metabotype was associated with IBD and was characterized by increased bile acids, taurine, and tryptophan. The IBD-associated microbial and metabolomics states were highly correlated, suggesting that they represented an integrated ecosystem. Healthy relatives with the IBD-associated microbial community type had an increased incidence of elevated fecal calprotectin. Healthy first-degree relatives can have dysbiosis associated with an altered intestinal metabolome that may signify a predisease microbial susceptibility state or subclinical inflammation. Longitudinal prospective

Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Metabolomics in Sepsis and Its Impact on Public Health.

Science.gov (United States)

Evangelatos, Nikolaos; Bauer, Pia; Reumann, Matthias; Satyamoorthy, Kapaettu; Lehrach, Hans; Brand, Angela

2017-01-01

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software. © 2018 S. Karger AG, Basel.

SECIMTools: a suite of metabolomics data analysis tools.

Science.gov (United States)

Kirpich, Alexander S; Ibarra, Miguel; Moskalenko, Oleksandr; Fear, Justin M; Gerken, Joseph; Mi, Xinlei; Ashrafi, Ali; Morse, Alison M; McIntyre, Lauren M

2018-04-20

Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists. SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.

Rice Bran Metabolome Contains Amino Acids, Vitamins & Cofactors, and Phytochemicals with Medicinal and Nutritional Properties.

Science.gov (United States)

Zarei, Iman; Brown, Dustin G; Nealon, Nora Jean; Ryan, Elizabeth P

2017-12-01

Rice bran is a functional food that has shown protection against major chronic diseases (e.g. obesity, diabetes, cardiovascular disease and cancer) in animals and humans, and these health effects have been associated with the presence of bioactive phytochemicals. Food metabolomics uses multiple chromatography and mass spectrometry platforms to detect and identify a diverse range of small molecules with high sensitivity and precision, and has not been completed for rice bran. This study utilized global, non-targeted metabolomics to identify small molecules in rice bran, and conducted a comprehensive search of peer-reviewed literature to determine bioactive compounds. Three U.S. rice varieties (Calrose, Dixiebelle, and Neptune), that have been used for human dietary intervention trials, were assessed herein for bioactive compounds that have disease control and prevention properties. The profiling of rice bran by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) identified 453 distinct phytochemicals, 209 of which were classified as amino acids, cofactors & vitamins, and secondary metabolites, and were further assessed for bioactivity. A scientific literature search revealed 65 compounds with health properties, 16 of which had not been previously identified in rice bran. This suite of amino acids, cofactors & vitamins, and secondary metabolites comprised 46% of the identified rice bran metabolome, which substantially enhanced our knowledge of health-promoting rice bran compounds provided during dietary supplementation. Rice bran metabolite profiling revealed a suite of biochemical molecules that can be further investigated and exploited for multiple nutritional therapies and medical food applications. These bioactive compounds may also be biomarkers of dietary rice bran intake. The medicinal compounds associated with rice bran can function as a network across metabolic pathways and this

Vitamins, metabolomics, and prostate cancer.

Science.gov (United States)

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.

Metabolomics study on primary dysmenorrhea patients during the luteal regression stage based on ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry

Science.gov (United States)

Fang, Ling; Gu, Caiyun; Liu, Xinyu; Xie, Jiabin; Hou, Zhiguo; Tian, Meng; Yin, Jia; Li, Aizhu; Li, Yubo

2017-01-01

Primary dysmenorrhea (PD) is a common gynecological disorder which, while not life-threatening, severely affects the quality of life of women. Most patients with PD suffer ovarian hormone imbalances caused by uterine contraction, which results in dysmenorrhea. PD patients may also suffer from increases in estrogen levels caused by increased levels of prostaglandin synthesis and release during luteal regression and early menstruation. Although PD pathogenesis has been previously reported on, these studies only examined the menstrual period and neglected the importance of the luteal regression stage. Therefore, the present study used urine metabolomics to examine changes in endogenous substances and detect urine biomarkers for PD during luteal regression. Ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry was used to create metabolomic profiles for 36 patients with PD and 27 healthy controls. Principal component analysis and partial least squares discriminate analysis were used to investigate the metabolic alterations associated with PD. Ten biomarkers for PD were identified, including ornithine, dihydrocortisol, histidine, citrulline, sphinganine, phytosphingosine, progesterone, 17-hydroxyprogesterone, androstenedione, and 15-keto-prostaglandin F2α. The specificity and sensitivity of these biomarkers was assessed based on the area under the curve of receiver operator characteristic curves, which can be used to distinguish patients with PD from healthy controls. These results provide novel targets for the treatment of PD. PMID:28098892

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Introduction to metabolomics and its applications in ophthalmology

Science.gov (United States)

Tan, S Z; Begley, P; Mullard, G; Hollywood, K A; Bishop, P N

2016-01-01

Metabolomics is the study of endogenous and exogenous metabolites in biological systems, which aims to provide comparative semi-quantitative information about all metabolites in the system. Metabolomics is an emerging and potentially powerful tool in ophthalmology research. It is therefore important for health professionals and researchers involved in the speciality to understand the basic principles of metabolomics experiments. This article provides an overview of the experimental workflow and examples of its use in ophthalmology research from the study of disease metabolism and pathogenesis to identification of biomarkers. PMID:26987591

Linking metabolomics data to underlying metabolic regulation

Directory of Open Access Journals (Sweden)

Thomas eNägele

2014-11-01

Full Text Available The comprehensive experimental analysis of a metabolic constitution plays a central role in approaches of organismal systems biology.Quantifying the impact of a changing environment on the homeostasis of cellular metabolism has been the focus of numerous studies applying various metabolomics techniques. It has been proven that approaches which integrate different analytical techniques, e.g. LC-MS, GC-MS, CE-MS and H-NMR, can provide a comprehensive picture of a certain metabolic homeostasis. Identification of metabolic compounds and quantification of metabolite levels represent the groundwork for the analysis of regulatory strategies in cellular metabolism. This significantly promotes our current understanding of the molecular organization and regulation of cells, tissues and whole organisms.Nevertheless, it is demanding to elicit the pertinent information which is contained in metabolomics data sets.Based on the central dogma of molecular biology, metabolite levels and their fluctuations are the result of a directed flux of information from gene activation over transcription to translation and posttranslational modification.Hence, metabolomics data represent the summed output of a metabolic system comprising various levels of molecular organization.As a consequence, the inverse assignment of metabolomics data to underlying regulatory processes should yield information which-if deciphered correctly-provides comprehensive insight into a metabolic system.Yet, the deduction of regulatory principles is complex not only due to the high number of metabolic compounds, but also because of a high level of cellular compartmentalization and differentiation.Motivated by the question how metabolomics approaches can provide a representative view on regulatory biochemical processes, this article intends to present and discuss current metabolomics applications, strategies of data analysis and their limitations with respect to the interpretability in context of

MetaboLights: An Open-Access Database Repository for Metabolomics Data.

Science.gov (United States)

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.

Tools and Databases of the KOMICS Web Portal for Preprocessing, Mining, and Dissemination of Metabolomics Data

Directory of Open Access Journals (Sweden)

Nozomu Sakurai

2014-01-01

Full Text Available A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal, where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.

Day-3 embryo metabolomics in the spent culture media is altered in obese women undergoing in vitro fertilization.

Science.gov (United States)

Bellver, José; De Los Santos, María J; Alamá, Pilar; Castelló, Damià; Privitera, Laura; Galliano, Daniela; Labarta, Elena; Vidal, Carmen; Pellicer, Antonio; Domínguez, Francisco

2015-06-01

To determine whether the global metabolomic profile of the spent culture media (SCM) of day-3 embryos is different in obese and normoweight women undergoing in vitro fertilization (IVF). Prospective cohort analysis. IVF clinic. Twenty-eight young, nonsmoking women with normoweight, nonsmoking male partners with mild/normal sperm factors undergoing a first IVF attempt for idiopathic infertility, tubal factor infertility, or failed ovulation induction: obese ovulatory women (n = 12); obese women with polycystic ovary syndrome (PCOS; n = 4); normoweight ovulatory women (n = 12). Fifty μl of SCM collected from two day-3 embryos of each cohort. Metabolomic profiling via ultrahigh performance liquid chromatography coupled to mass spectrometry of SCM from a total of 56 embryos. The untargeted metabolomic profile was different in obese and normoweight women. Partial least squares discriminant analysis resulted in a clear separation of samples when a total of 551 differential metabolites were considered. A prediction model was generated using the most consistent metabolites. Most of the metabolites identified were saturated fatty acids, which were detected in lower concentrations in the SCM of embryos from obese women. The metabolomic profile was similar in obese women with or without PCOS. The metabolomic profile in the SCM of day-3 embryos is different in normoweight and obese women. Saturated fatty acids seem to be reduced when embryos from obese patients are present. NCT01448863. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

Metabolomics to study functional consequences in peroxisomal disorders

NARCIS (Netherlands)

Herzog, K.

2017-01-01

This thesis focusses on metabolomics approaches performed in cultured cells and blood samples from patients with peroxisomal disorders. By applying both targeted and untargeted metabolomics, the aim of these approaches was to study the functional consequences of the primary genetic defects causing

Decoding the dynamics of cellular metabolism and the action of 3-bromopyruvate and 2-deoxyglucose using pulsed stable isotope-resolved metabolomics.

Science.gov (United States)

Pietzke, Matthias; Zasada, Christin; Mudrich, Susann; Kempa, Stefan

2014-01-01

Cellular metabolism is highly dynamic and continuously adjusts to the physiological program of the cell. The regulation of metabolism appears at all biological levels: (post-) transcriptional, (post-) translational, and allosteric. This regulatory information is expressed in the metabolome, but in a complex manner. To decode such complex information, new methods are needed in order to facilitate dynamic metabolic characterization at high resolution. Here, we describe pulsed stable isotope-resolved metabolomics (pSIRM) as a tool for the dynamic metabolic characterization of cellular metabolism. We have adapted gas chromatography-coupled mass spectrometric methods for metabolomic profiling and stable isotope-resolved metabolomics. In addition, we have improved robustness and reproducibility and implemented a strategy for the absolute quantification of metabolites. By way of examples, we have applied this methodology to characterize central carbon metabolism of a panel of cancer cell lines and to determine the mode of metabolic inhibition of glycolytic inhibitors in times ranging from minutes to hours. Using pSIRM, we observed that 2-deoxyglucose is a metabolic inhibitor, but does not directly act on the glycolytic cascade.

A Metabolome-Wide Study of Dry Eye Disease Reveals Serum Androgens as Biomarkers.

Science.gov (United States)

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

Analysis of stable isotope assisted metabolomics data acquired by GC-MS

International Nuclear Information System (INIS)

Wei, Xiaoli; Shi, Biyun; Koo, Imhoi; Yin, Xinmin; Lorkiewicz, Pawel; Suhail, Hamid; Rattan, Ramandeep; Giri, Shailendra; McClain, Craig J.

2017-01-01

Stable isotope assisted metabolomics (SIAM) measures the abundance levels of metabolites in a particular pathway using stable isotope tracers (e.g., 13 C, 18 O and/or 15 N). We report a method termed signature ion approach for analysis of SIAM data acquired on a GC-MS system equipped with an electron ionization (EI) ion source. The signature ion is a fragment ion in EI mass spectrum of a derivatized metabolite that contains all atoms of the underivatized metabolite, except the hydrogen atoms lost during derivatization. In this approach, GC-MS data of metabolite standards were used to recognize the signature ion from the EI mass spectra acquired from stable isotope labeled samples, and a linear regression model was used to deconvolute the intensity of overlapping isotopologues. A mixture score function was also employed for cross-sample chromatographic peak list alignment to recognize the chromatographic peaks generated by the same metabolite in different samples, by simultaneously evaluating the similarity of retention time and EI mass spectrum of two chromatographic peaks. Analysis of a mixture of 16 13 C-labeled and 16 unlabeled amino acids showed that the signature ion approach accurately identified and quantified all isotopologues. Analysis of polar metabolite extracts from cells respectively fed with uniform 13 C-glucose and 13 C-glutamine further demonstrated that this method can also be used to analyze the complex data acquired from biological samples. - Highlights: • A signature ion approach is developed for analysis of stable isotope GC-MS data. • GC-MS data of compound standards are used for selection of the signature ion. • Linear regression model is used to deconvolute the overlapping isotopologue peaks. • The developed method was tested by known compounds and biological samples.

Induced pluripotent stem cells show metabolomic differences to embryonic stem cells in polyunsaturated phosphatidylcholines and primary metabolism.

Directory of Open Access Journals (Sweden)

John K Meissen

Full Text Available Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses. Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS, gas chromatography-time of flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine the metabolome of induced pluripotent stem cells (iPSCs compared to parental fibroblasts as well as to reference embryonic stem cells (ESCs. With over 250 identified metabolites and a range of structurally unknown compounds, quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids. However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanolamine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis. Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and induction of pluripotency.

[Development of Plant Metabolomics and Medicinal Plant Genomics].

Science.gov (United States)

Saito, Kazuki

2018-01-01

 A variety of chemicals produced by plants, often referred to as 'phytochemicals', have been used as medicines, food, fuels and industrial raw materials. Recent advances in the study of genomics and metabolomics in plant science have accelerated our understanding of the mechanisms, regulation and evolution of the biosynthesis of specialized plant products. We can now address such questions as how the metabolomic diversity of plants is originated at the levels of genome, and how we should apply this knowledge to drug discovery, industry and agriculture. Our research group has focused on metabolomics-based functional genomics over the last 15 years and we have developed a new research area called 'Phytochemical Genomics'. In this review, the development of a research platform for plant metabolomics is discussed first, to provide a better understanding of the chemical diversity of plants. Then, representative applications of metabolomics to functional genomics in a model plant, Arabidopsis thaliana, are described. The extension of integrated multi-omics analyses to non-model specialized plants, e.g., medicinal plants, is presented, including the identification of novel genes, metabolites and networks for the biosynthesis of flavonoids, alkaloids, sulfur-containing metabolites and terpenoids. Further, functional genomics studies on a variety of medicinal plants is presented. I also discuss future trends in pharmacognosy and related sciences.

Metabolomics analysis of metabolic effects of nicotinamide phosphoribosyltransferase (NAMPT inhibition on human cancer cells.

Directory of Open Access Journals (Sweden)

Vladimir Tolstikov

Full Text Available 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.

Microbiome, Metabolome and Inflammatory Bowel Disease

Directory of Open Access Journals (Sweden)

Ishfaq Ahmed

2016-06-01

Full Text Available Inflammatory Bowel Disease (IBD is a multifactorial disorder that conceptually occurs as a result of altered immune responses to commensal and/or pathogenic gut microbes in individuals most susceptible to the disease. During Crohn’s Disease (CD or Ulcerative Colitis (UC, two components of the human IBD, distinct stages define the disease onset, severity, progression and remission. Epigenetic, environmental (microbiome, metabolome and nutritional factors are important in IBD pathogenesis. While the dysbiotic microbiota has been proposed to play a role in disease pathogenesis, the data on IBD and diet are still less convincing. Nonetheless, studies are ongoing to examine the effect of pre/probiotics and/or FODMAP reduced diets on both the gut microbiome and its metabolome in an effort to define the healthy diet in patients with IBD. Knowledge of a unique metabolomic fingerprint in IBD could be useful for diagnosis, treatment and detection of disease pathogenesis.

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

International Nuclear Information System (INIS)

Kumar, Bhowmik Salil; Lee, Young-Joo; Yi, Hong Jae; Chung, Bong Chul; Jung, Byung Hwa

2010-01-01

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg -1 day -1 or 250 mg kg -1 day -1 for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach

Energy Technology Data Exchange (ETDEWEB)

Kumar, Bhowmik Salil [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of); Lee, Young-Joo; Yi, Hong Jae [College of Pharmacy, Kyung Hee University, Hoegi-dong, Dongdaemun-gu, Seoul 130-791 (Korea, Republic of); Chung, Bong Chul [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); Jung, Byung Hwa, E-mail: jbhluck@kist.re.kr [Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650 (Korea, Republic of); University of Science and Technology, (305-333) 113 Gwahangno, Yuseong-gu, Daejeon (Korea, Republic of)

2010-02-19

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg{sup -1} day{sup -1} or 250 mg kg{sup -1} day{sup -1} for a period of 7 days (n = 4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers.

Diurnal effects of anoxia on the metabolome of the seagrass Zostera marina

DEFF Research Database (Denmark)

Hasler-Sheetal, Harald; Holmer, Marianne; Weckwerth, Wolfram

2014-01-01

Environmental metabolomics has become interesting in marine ecological studies. One example is the revealing of new insights in stress response of Zostera marina. This is essential to understand how, at which level and to what extend aquatic plants adapt, tolerate and react to environmental...... stressors. We exposed Z. marina to water column anoxia and assessed the diurnal metabolomic response by GC-TOF-MS based metabolomics identifying 109 known and 217 unknown metabolites. During day time photosynthetic oxygen production prevents severe effects of anoxia on the metabolome (complete set of small...... the applicability of metabolomics to assess environmental stress responses of Zostera marina....

A Review of Applications of Metabolomics in Cancer

Directory of Open Access Journals (Sweden)

Richard D. Beger

2013-07-01

Full Text Available Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.

Metabolomics Workbench (MetWB)

Data.gov (United States)

U.S. Department of Health & Human Services — The Metabolomics Program's Data Repository and Coordinating Center (DRCC), housed at the San Diego Supercomputer Center (SDSC), University of California, San Diego,...

Metabolome analysis of Drosophila melanogaster during embryogenesis.

Science.gov (United States)

An, Phan Nguyen Thuy; Yamaguchi, Masamitsu; Bamba, Takeshi; Fukusaki, Eiichiro

2014-01-01

The Drosophila melanogaster embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of Drosophila embryogenesis. Therefore, the aim of this study is to assess the changes in embryos' metabolome that occur at different stages of the Drosophila embryonic development. Time course samples of Drosophila embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of Drosophila embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of Drosophila embryo.

Metabolomic Profiles of Dinophysis acuminata and Dinophysis acuta Using Non-Targeted High-Resolution Mass Spectrometry

DEFF Research Database (Denmark)

García-Portela, María; Reguera, Beatriz; Sibat, Manoella

2018-01-01

Photosynthetic species of the genus Dinophysis are obligate mixotrophs with temporary plastids (kleptoplastids) that are acquired from the ciliate Mesodinium rubrum, which feeds on cryptophytes of the Teleaulax-Plagioselmis-Geminigera clade. A metabolomic study of the three-species food chain Din...

Plant Metabolomics : the missiong link in functional genomics strategies

NARCIS (Netherlands)

Hall, R.D.; Beale, M.; Fiehn, O.; Hardy, N.; Summer, L.; Bino, R.

2002-01-01

After the establishment of technologies for high-throughput DNA sequencing (genomics), gene expression analysis (transcriptomics), and protein analysis (proteomics), the remaining functional genomics challenge is that of metabolomics. Metabolomics is the term coined for essentially comprehensive,

Accurate evolutions of inspiralling and magnetized neutron stars: Equal-mass binaries

International Nuclear Information System (INIS)

Giacomazzo, Bruno; Rezzolla, Luciano; Baiotti, Luca

2011-01-01

By performing new, long and numerically accurate general-relativistic simulations of magnetized, equal-mass neutron-star binaries, we investigate the role that realistic magnetic fields may have in the evolution of these systems. In particular, we study the evolution of the magnetic fields and show that they can influence the survival of the hypermassive neutron star produced at the merger by accelerating its collapse to a black hole. We also provide evidence that, even if purely poloidal initially, the magnetic fields produced in the tori surrounding the black hole have toroidal and poloidal components of equivalent strength. When estimating the possibility that magnetic fields could have an impact on the gravitational-wave signals emitted by these systems either during the inspiral or after the merger, we conclude that for realistic magnetic-field strengths B 12 G such effects could be detected, but only marginally, by detectors such as advanced LIGO or advanced Virgo. However, magnetically induced modifications could become detectable in the case of small-mass binaries and with the development of gravitational-wave detectors, such as the Einstein Telescope, with much higher sensitivities at frequencies larger than ≅2 kHz.

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

Science.gov (United States)

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 Study of Resina Draconis on Myocardial Ischemia Rats Using Ultraperformance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry Combined with Pattern Recognition Methods and Metabolic Pathway Analysis

Directory of Open Access Journals (Sweden)

Yunpeng Qi

2013-01-01

Full Text Available Resina draconis (bright red resin isolated from Dracaena cochinchinensis, RD has been clinically used for treatment of myocardial ischemia (MI for many years. However, the mechanisms of its pharmacological action on MI are still poorly understood. This study aimed to characterize the plasma metabolic profiles of MI and investigate the mechanisms of RD on MI using ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry-based metabolomics combined with pattern recognition methods and metabolic pathway analysis. Twenty metabolite markers characterizing metabolic profile of MI were revealed, which were mainly involved in aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, vascular smooth muscle contraction, sphingolipid metabolism, and so forth. After RD treatment, however, levels of seven MI metabolite markers, including phytosphingosine, sphinganine, acetylcarnitine, cGMP, cAMP, L-tyrosine, and L-valine, were turned over, indicating that RD is likely to alleviate MI through regulating the disturbed vascular smooth muscle contraction, sphingolipid metabolism, phenylalanine metabolism, and BCAA metabolism. To our best knowledge, this is the first comprehensive study to investigate the mechanisms of RD for treating MI, from a metabolomics point of view. Our findings are very valuable to gain a better understanding of MI metabolic profiles and provide novel insights for exploring the mechanisms of RD on MI.

Deconstructing the pig sex metabolome: Targeted metabolomics in heavy pigs revealed sexual dimorphisms in plasma biomarkers and metabolic pathways.

Science.gov (United States)

Bovo, S; Mazzoni, G; Calò, D G; Galimberti, G; Fanelli, F; Mezzullo, M; Schiavo, G; Scotti, E; Manisi, A; Samoré, A B; Bertolini, F; Trevisi, P; Bosi, P; Dall'Olio, S; Pagotto, U; Fontanesi, L

2015-12-01

Metabolomics has opened new possibilities to investigate metabolic differences among animals. In this study, we applied a targeted metabolomic approach to deconstruct the pig sex metabolome as defined by castrated males and entire gilts. Plasma from 545 performance-tested Italian Large White pigs (172 castrated males and 373 females) sampled at about 160 kg live weight were analyzed for 186 metabolites using the Biocrates AbsoluteIDQ p180 Kit. After filtering, 132 metabolites (20 AA, 11 biogenic amines, 1 hexose, 13 acylcarnitines, 11 sphingomyelins, 67 phosphatidylcholines, and 9 lysophosphatidylcholines) were retained for further analyses. The multivariate approach of the sparse partial least squares discriminant analysis was applied, together with a specifically designed statistical pipeline, that included a permutation test and a 10 cross-fold validation procedure that produced stability and effect size statistics for each metabolite. Using this approach, we identified 85 biomarkers (with metabolites from all analyzed chemical families) that contributed to the differences between the 2 groups of pigs ( metabolic shift in castrated males toward energy storage and lipid production. Similar general patterns were observed for most sphingomyelins, phosphatidylcholines, and lysophosphatidylcholines. Metabolomic pathway analysis and pathway enrichment identified several differences between the 2 sexes. This metabolomic overview opened new clues on the biochemical mechanisms underlying sexual dimorphism that, on one hand, might explain differences in terms of economic traits between castrated male pigs and entire gilts and, on the other hand, could strengthen the pig as a model to define metabolic mechanisms related to fat deposition.

Metabolomics, a promising approach to translational research in cardiology

Directory of Open Access Journals (Sweden)

Martino Deidda

2015-12-01

In this article, we will provide a description of metabolomics in comparison with other, better known “omics” disciplines such as genomics and proteomics. In addition, we will review the current rationale for the implementation of metabolomics in cardiology, its basic methodology and the available data from human studies in this discipline. The topics covered will delineate the importance of being able to use the metabolomic information to understand the mechanisms of diseases from the perspective of systems biology, and as a non-invasive approach to the diagnosis, grading and treatment of cardiovascular diseases.

Metabolomic Profiles of Aspergillus oryzae and Bacillus amyloliquefaciens During Rice Koji Fermentation

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Da Eun Lee

2016-06-01

Full Text Available 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.

The effect of gluten on the host-microbial metabolism assessed by urinary metabolomics

DEFF Research Database (Denmark)

Roager, Henrik Munch; Frandsen, Henrik Lauritz; Gøbel, Rikke Juul

A gluten-free diet clearly improves the life of patients with celiac disease, but the scientific evidence supporting possible health benefits of a gluten-free diet for non-celiac adults is limited. Therefore, as urine reflects the host and gut microbial metabolism, the study aimed to assess...... a gluten-rich (21.6±5.7g/day) or a gluten-poor (~1g/day) diet for 8 weeks, crossing over to the other diet after 6 weeks washout. Urine samples were standardised collected at the beginning and end of each diet intervention period and were analysed by gas chromatography mass spectrometry (GC-MS) and liquid...... the long-term metabolic effect of gluten on the urine metabolome of non-celiac individuals by a cross-over intervention study (gluten-poor and gluten rich, respectively) using a non-targeted metabolomics approach. Fifty-one non-celiac adult participants (30 female, 21 male) were randomized to either...

Metabolome Integrated Analysis of High-Temperature Response in Pinus radiata

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Mónica Escandón

2018-04-01

Full Text Available 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.

Circadian Metabolomics in Time and Space

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Kenneth A. Dyar

2017-07-01

Full Text Available Circadian rhythms are widely known to govern human health and disease, but specific pathogenic mechanisms linking circadian disruption to metabolic diseases are just beginning to come to light. This is thanks in part to the development and application of various “omics”-based tools in biology and medicine. Current high-throughput technologies allow for the simultaneous monitoring of multiple dynamic cellular events over time, ranging from gene expression to metabolite abundance and sub-cellular localization. These fundamental temporal and spatial perspectives have allowed for a more comprehensive understanding of how various dynamic cellular events and biochemical processes are related in health and disease. With advances in technology, metabolomics has become a more routine “omics” approach for studying metabolism, and “circadian metabolomics” (i.e., studying the 24-h metabolome has recently been undertaken by several groups. To date, circadian metabolomes have been reported for human serum, saliva, breath, and urine, as well as tissues from several species under specific disease or mutagenesis conditions. Importantly, these studies have consistently revealed that 24-h rhythms are prevalent in almost every tissue and metabolic pathway. Furthermore, these circadian rhythms in tissue metabolism are ultimately linked to and directed by internal 24-h biological clocks. In this review, we will attempt to put these data-rich circadian metabolomics experiments into perspective to find out what they can tell us about metabolic health and disease, and what additional biomarker potential they may reveal.

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

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Matthias S. Klein

2016-01-01

Full Text Available 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.

Application of a Smartphone Metabolomics Platform to the Authentication of Schisandra sinensis.

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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.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review.

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Li, Bingbing; He, Xuyun; Jia, Wei; Li, Houkai

2017-07-13

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Metabolomic Elucidation of the Effects of Curcumin on Fibroblast-Like Synoviocytes in Rheumatoid Arthritis.

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Joong Kyong Ahn

Full Text Available Rheumatoid arthritis (RA is a chronic systemic inflammatory disease characterized by synovial inflammation and joint disability. Curcumin is known to be effective in ameliorating joint inflammation in RA. To obtain new insights into the effect of curcumin on primary fibroblast-like synoviocytes (FLS, N = 3, which are key effector cells in RA, we employed gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS-based metabolomics. Metabolomic profiling of tumor necrosis factor (TNF-α-stimulated and curcumin-treated FLS was performed using GC/TOF-MS in conjunction with univariate and multivariate statistical analyses. A total of 119 metabolites were identified. Metabolomic analysis revealed that metabolite profiles were clearly distinct between TNF-α-stimulated vs. the control group (not stimulated by TNF-α or curcumin. Treatment of FLS with curcumin showed that the metabolic perturbation by TNF-α could be reversed to that of the control group to a considerable extent. Curcumin-treated FLS had higher restoration of amino acid and fatty acid metabolism, as indicated by the prominent metabolic restoration of intermediates of amino acid and fatty acid metabolism, compared with that observed in TNF-α-stimulated FLS. In particular, the abundance of glycine, citrulline, arachidonic acid, and saturated fatty acids in TNF-α-stimulated FLS was restored to the control level after treatment with curcumin, suggesting that the effect of curcumin on preventing joint inflammation may be elucidated with the levels of these metabolites. Our results suggest that GC/TOF-MS-based metabolomic investigation using FLS has the potential for discovering the mechanism of action of curcumin and new targets for therapeutic drugs in RA.

Metabolomic Elucidation of the Effects of Curcumin on Fibroblast-Like Synoviocytes in Rheumatoid Arthritis.

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Ahn, Joong Kyong; Kim, Sooah; Hwang, Jiwon; Kim, Jungyeon; Lee, You Sun; Koh, Eun-Mi; Kim, Kyoung Heon; Cha, Hoon-Suk

2015-01-01

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease characterized by synovial inflammation and joint disability. Curcumin is known to be effective in ameliorating joint inflammation in RA. To obtain new insights into the effect of curcumin on primary fibroblast-like synoviocytes (FLS, N = 3), which are key effector cells in RA, we employed gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS)-based metabolomics. Metabolomic profiling of tumor necrosis factor (TNF)-α-stimulated and curcumin-treated FLS was performed using GC/TOF-MS in conjunction with univariate and multivariate statistical analyses. A total of 119 metabolites were identified. Metabolomic analysis revealed that metabolite profiles were clearly distinct between TNF-α-stimulated vs. the control group (not stimulated by TNF-α or curcumin). Treatment of FLS with curcumin showed that the metabolic perturbation by TNF-α could be reversed to that of the control group to a considerable extent. Curcumin-treated FLS had higher restoration of amino acid and fatty acid metabolism, as indicated by the prominent metabolic restoration of intermediates of amino acid and fatty acid metabolism, compared with that observed in TNF-α-stimulated FLS. In particular, the abundance of glycine, citrulline, arachidonic acid, and saturated fatty acids in TNF-α-stimulated FLS was restored to the control level after treatment with curcumin, suggesting that the effect of curcumin on preventing joint inflammation may be elucidated with the levels of these metabolites. Our results suggest that GC/TOF-MS-based metabolomic investigation using FLS has the potential for discovering the mechanism of action of curcumin and new targets for therapeutic drugs in RA.

Metabolomic characteristics of arsenic-associated diabetes in a prospective cohort in Chihuahua, Mexico.

Science.gov (United States)

Martin, Elizabeth; González-Horta, Carmen; Rager, Julia; Bailey, Kathryn A; Sánchez-Ramírez, Blanca; Ballinas-Casarrubias, Lourdes; Ishida, María C; Gutiérrez-Torres, Daniela S; Hernández Cerón, Roberto; Viniegra Morales, Damián; Baeza Terrazas, Francisco A; Saunders, R Jesse; Drobná, Zuzana; Mendez, Michelle A; Buse, John B; Loomis, Dana; Jia, Wei; García-Vargas, Gonzalo G; Del Razo, Luz M; Stýblo, Miroslav; Fry, Rebecca

2015-04-01

Chronic exposure to inorganic arsenic (iAs) has been linked to an increased risk of diabetes, yet the specific disease phenotype and underlying mechanisms are poorly understood. In the present study we set out to identify iAs exposure-associated metabolites with altered abundance in nondiabetic and diabetic individuals in an effort to understand the relationship between exposure, metabolomic response, and disease status. A nested study design was used to profile metabolomic shifts in urine and plasma collected from 90 diabetic and 86 nondiabetic individuals matched for varying iAs concentrations in drinking water, body mass index, age, and sex. Diabetes diagnosis was based on measures of fasting plasma glucose and 2-h blood glucose. Multivariable models were used to identify metabolites with altered abundance associated with iAs exposure among diabetic and nondiabetic individuals. A total of 132 metabolites were identified to shift in urine or plasma in response to iAs exposure characterized by the sum of iAs metabolites in urine (U-tAs). Although many metabolites were altered in both diabetic and nondiabetic 35 subjects, diabetic individuals displayed a unique response to iAs exposure with 59 altered metabolites including those that play a role in tricarboxylic acid cycle and amino acid metabolism. Taken together, these data highlight the broad impact of iAs exposure on the human metabolome, and demonstrate some specificity of the metabolomic response between diabetic and nondiabetic individuals. These data may provide novel insights into the mechanisms and phenotype of diabetes associated with iAs exposure. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis.

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Xia, Jianguo; Wishart, David S

2016-09-07

MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Empirical Accurate Masses and Radii of Single Stars with TESS and Gaia

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Stassun, Keivan G.; Corsaro, Enrico; Pepper, Joshua A.; Gaudi, B. Scott

2018-01-01

We present a methodology for the determination of empirical masses of single stars through the combination of three direct observables with Gaia and Transiting Exoplanet Survey Satellite (TESS): (i) the surface gravity via granulation-driven variations in the TESS light curve, (ii) the bolometric flux at Earth via the broadband spectral energy distribution, and (iii) the distance via the Gaia parallax. We demonstrate the method using 525 Kepler stars for which these measures are available in the literature, and show that the stellar masses can be measured with this method to a precision of ∼25%, limited by the surface-gravity precision of the granulation “flicker” method (∼0.1 dex) and by the parallax uncertainties (∼10% for the Kepler sample). We explore the impact of expected improvements in the surface gravity determinations—through the application of granulation background fitting and the use of recently published granulation-metallicity relations—and improvements in the parallaxes with the arrival of the Gaia second data release. We show that the application of this methodology to stars that will be observed by TESS should yield radii good to a few percent and masses good to ≈10%. Importantly, the method does not require the presence of an orbiting, eclipsing, or transiting body, nor does it require spatial resolution of the stellar surface. Thus, we can anticipate the determination of fundamental, accurate stellar radii and masses for hundreds of thousands of bright single stars—across the entire sky and spanning the Hertzsprung–Russell diagram—including those that will ultimately be found to host planets.

A metabolomics study on human dietary intervention with apples

DEFF Research Database (Denmark)

Dragsted, L. O.; Kristensen, M.; Ravn-Haren, Gitte

2009-01-01

Metabolomics is a promising tool for searching out new biomarkers and the development of hypotheses in nutrition research. This chapter will describe the design of human dietary intervention studies where samples are collected for metabolomics analyses as well as the analytical issues and data...

Plant metabolomics and its potential application for human nutrition

NARCIS (Netherlands)

Hall, R.D.; Brouwer, I.D.; Fitzgerald, M.A.

2008-01-01

With the growing interest in the use of metabolomic technologies for a wide range of biological targets, food applications related to nutrition and quality are rapidly emerging. Metabolomics offers us the opportunity to gain deeper insights into, and have better control of, the fundamental

Profiling the Oxylipin and Endocannabinoid Metabolome by UPLC-ESI-MS/MS in Human Plasma to Monitor Postprandial Inflammation.

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Gouveia-Figueira, Sandra; Späth, Jana; Zivkovic, Angela M; Nording, Malin L

2015-01-01

Bioactive lipids, including oxylipins, endocannabinoids, and related compounds may function as specific biochemical markers of certain aspects of inflammation. However, the postprandial responsiveness of these compounds is largely unknown; therefore, changes in the circulating oxylipin and endocannabinoid metabolome in response to a challenge meal were investigated at six occasions in a subject who freely modified her usual diet. The dietary change, and especially the challenge meal itself, represented a modification of precursor fatty acid status, with expectedly subtle effects on bioactive lipid levels. To detect even the slightest alteration, highly sensitive ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization (ESI) tandem mass spectrometry (MS/MS) methods for bioactive lipid profiling was employed. A previously validated UPLC-ESI-MS/MS method for profiling the endocannabinoid metabolome was used, while validation of an UPLC-ESI-MS/MS method for oxylipin analysis was performed with acceptable outcomes for a majority of the parameters according to the US Food and Drug Administration guidelines for linearity (0.9938 metabolome, caused by changes in diet and ii) responsiveness to a challenge meal for a subset of the oxylipin and endocannabinoid metabolome. To summarize, we have shown proof-of-concept of our UPLC-ESI-MS/MS bioactive lipid protocols for the purpose of monitoring subtle shifts, and thereby useful to address lipid-mediated postprandial inflammation.

High-resolution metabolomics of occupational exposure to trichloroethylene.

Science.gov (United States)

Walker, Douglas I; Uppal, Karan; Zhang, Luoping; Vermeulen, Roel; Smith, Martyn; Hu, Wei; Purdue, Mark P; Tang, Xiaojiang; Reiss, Boris; Kim, Sungkyoon; Li, Laiyu; Huang, Hanlin; Pennell, Kurt D; Jones, Dean P; Rothman, Nathaniel; Lan, Qing

2016-10-01

Occupational exposure to trichloroethylene (TCE) has been linked to adverse health outcomes including non-Hodgkin's lymphoma and kidney and liver cancer; however, TCE's mode of action for development of these diseases in humans is not well understood. Non-targeted metabolomics analysis of plasma obtained from 80 TCE-exposed workers [full shift exposure range of 0.4 to 230 parts-per-million of air (ppm a )] and 95 matched controls were completed by ultra-high resolution mass spectrometry. Biological response to TCE exposure was determined using a metabolome-wide association study (MWAS) framework, with metabolic changes and plasma TCE metabolites evaluated by dose-response and pathway enrichment. Biological perturbations were then linked to immunological, renal and exposure molecular markers measured in the same population. Metabolic features associated with TCE exposure included known TCE metabolites, unidentifiable chlorinated compounds and endogenous metabolites. Exposure resulted in a systemic response in endogenous metabolism, including disruption in purine catabolism and decreases in sulphur amino acid and bile acid biosynthesis pathways. Metabolite associations with TCE exposure included uric acid (β = 0.13, P-value = 3.6 × 10 -5 ), glutamine (β = 0.08, P-value = 0.0013), cystine (β = 0.75, P-value = 0.0022), methylthioadenosine (β = -1.6, P-value = 0.0043), taurine (β = -2.4, P-value = 0.0011) and chenodeoxycholic acid (β = -1.3, P-value = 0.0039), which are consistent with known toxic effects of TCE, including immunosuppression, hepatotoxicity and nephrotoxicity. Correlation with additional exposure markers and physiological endpoints supported known disease associations. High-resolution metabolomics correlates measured occupational exposure to internal dose and metabolic response, providing insight into molecular mechanisms of exposure-related disease aetiology. © The Author 2016; all rights

Metabolomics window into diabetic complications.

Science.gov (United States)

Wu, Tao; Qiao, Shuxuan; Shi, Chenze; Wang, Shuya; Ji, Guang

2018-03-01

Diabetes has become a major global health problem. The elucidation of characteristic metabolic alterations during the diabetic progression is critical for better understanding its pathogenesis, and identifying potential biomarkers and drug targets. Metabolomics is a promising tool to reveal the metabolic changes and the underlying mechanism involved in the pathogenesis of diabetic complications. The present review provides an update on the application of metabolomics in diabetic complications, including diabetic coronary artery disease, diabetic nephropathy, diabetic retinopathy and diabetic neuropathy, and this review provides notes on the prevention and prediction of diabetic complications. © 2017 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Statistical methods for the analysis of high-throughput metabolomics data

Directory of Open Access Journals (Sweden)

Fabian J. Theis

2013-01-01

Full Text Available Metabolomics is a relatively new high-throughput technology that aims at measuring all endogenous metabolites within a biological sample in an unbiased fashion. The resulting metabolic profiles may be regarded as functional signatures of the physiological state, and have been shown to comprise effects of genetic regulation as well as environmental factors. This potential to connect genotypic to phenotypic information promises new insights and biomarkers for different research fields, including biomedical and pharmaceutical research. In the statistical analysis of metabolomics data, many techniques from other omics fields can be reused. However recently, a number of tools specific for metabolomics data have been developed as well. The focus of this mini review will be on recent advancements in the analysis of metabolomics data especially by utilizing Gaussian graphical models and independent component analysis.

Human gut microbes impact host serum metabolome and insulin sensitivity

DEFF Research Database (Denmark)

Pedersen, Helle Krogh; Gudmundsdottir, Valborg; Nielsen, Henrik Bjørn

2016-01-01

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individ......Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin......-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus...

THE MEGAMASER COSMOLOGY PROJECT. III. ACCURATE MASSES OF SEVEN SUPERMASSIVE BLACK HOLES IN ACTIVE GALAXIES WITH CIRCUMNUCLEAR MEGAMASER DISKS

International Nuclear Information System (INIS)

Kuo, C. Y.; Braatz, J. A.; Condon, J. J.; Impellizzeri, C. M. V.; Lo, K. Y.; Zaw, I.; Schenker, M.; Henkel, C.; Reid, M. J.; Greene, J. E.

2011-01-01

Observations of H 2 O masers from circumnuclear disks in active galaxies for the Megamaser Cosmology Project (MCP) allow accurate measurement of the mass of supermassive black holes (BH) in these galaxies. We present the Very Long Baseline Interferometry images and kinematics of water maser emission in six active galaxies: NGC 1194, NGC 2273, NGC 2960 (Mrk 1419), NGC 4388, NGC 6264 and NGC 6323. We use the Keplerian rotation curves of these six megamaser galaxies, plus a seventh previously published, to determine accurate enclosed masses within the central ∼0.3 pc of these galaxies, smaller than the radius of the sphere of influence of the central mass in all cases. We also set lower limits to the central mass densities of between 0.12 x 10 10 and 61 x 10 10 M sun pc -3 . For six of the seven disks, the high central densities rule out clusters of stars or stellar remnants as the central objects, and this result further supports our assumption that the enclosed mass can be attributed predominantly to a supermassive BH. The seven BHs have masses ranging between 0.75 x 10 7 and 6.5 x 10 7 M sun , with the mass errors dominated by the uncertainty of the Hubble constant. We compare the megamaser BH mass determination with BH mass measured from the virial estimation method. The virial estimation BH mass in four galaxies is consistent with the megamaser BH mass, but the virial mass uncertainty is much greater. Circumnuclear megamaser disks allow the best mass determination of the central BH mass in external galaxies and significantly improve the observational basis at the low-mass end of the M-σ * relation. The M-σ * relation may not be a single, low-scatter power law as originally proposed. MCP observations continue and we expect to obtain more maser BH masses in the future.

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

Science.gov (United States)

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.

Implications of genotypic differences in the generation of a urinary metabolomics radiation signature

International Nuclear Information System (INIS)

Laiakis, Evagelia C.; Pannkuk, Evan L.; Diaz-Rubio, Maria Elena; Wang, Yi-Wen; Mak, Tytus D.; Simbulan-Rosenthal, Cynthia M.; Brenner, David J.; Fornace, Albert J.

2016-01-01

Highlights: • Metabolomics can rapidly assess the collective small molecule content in biofluids. • Previous studies focused on urine of wild type mice and now extending to radiosensitive mice. • The Parp1"−"/"− mice were utilized as a model radiosensitive mouse line. • Time-dependent perturbations are evident in different metabolic pathways. • Metabolomics can be used to dissect the effects of genotoxic agents on metabolism. - Abstract: The increased threat of radiological terrorism and accidental nuclear exposures, together with increased usage of radiation-based medical procedures, has made necessary the development of minimally invasive methods for rapid identification of exposed individuals. Genetically predisposed radiosensitive individuals comprise a significant number of the population and require specialized attention and treatments after such events. Metabolomics, the assessment of the collective small molecule content in a given biofluid or tissue, has proven effective in the rapid identification of radiation biomarkers and metabolic perturbations. To investigate how the genotypic background may alter the ionizing radiation (IR) signature, we analyzed urine from Parp1"−"/"− mice, as a model radiosensitive genotype, exposed to IR by utilizing the analytical power of liquid chromatography coupled with mass spectrometry (LC–MS), as urine has been thoroughly investigated in wild type (WT) mice in previous studies from our laboratory. Samples were collected at days one and three after irradiation, time points that are important for the early and efficient triage of exposed individuals. Time-dependent perturbations in metabolites were observed in the tricarboxylic acid pathway (TCA). Other differentially excreted metabolites included amino acids and metabolites associated with dysregulation of energy metabolism pathways. Time-dependent apoptotic pathway activation between WT and mutant mice following IR exposure may explain the altered

Implications of genotypic differences in the generation of a urinary metabolomics radiation signature

Energy Technology Data Exchange (ETDEWEB)

Laiakis, Evagelia C., E-mail: ecl28@georgetown.edu [Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington DC (United States); Pannkuk, Evan L. [Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington DC (United States); Diaz-Rubio, Maria Elena [Pediatrics, Division of Developmental Nutrition, University of Arkansas for Medical Sciences, Little Rock, AR (United States); Wang, Yi-Wen [Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington DC (United States); Mak, Tytus D. [Mass Spectrometry Data Center, National Institute of Standards and Technology (NIST), Gaithersburg MD (United States); Simbulan-Rosenthal, Cynthia M. [Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington DC (United States); Brenner, David J. [Columbia University, New York, NY (United States); Fornace, Albert J. [Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington DC (United States); Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC (United States); Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 22254 (Saudi Arabia)

2016-06-15

Highlights: • Metabolomics can rapidly assess the collective small molecule content in biofluids. • Previous studies focused on urine of wild type mice and now extending to radiosensitive mice. • The Parp1{sup −/−} mice were utilized as a model radiosensitive mouse line. • Time-dependent perturbations are evident in different metabolic pathways. • Metabolomics can be used to dissect the effects of genotoxic agents on metabolism. - Abstract: The increased threat of radiological terrorism and accidental nuclear exposures, together with increased usage of radiation-based medical procedures, has made necessary the development of minimally invasive methods for rapid identification of exposed individuals. Genetically predisposed radiosensitive individuals comprise a significant number of the population and require specialized attention and treatments after such events. Metabolomics, the assessment of the collective small molecule content in a given biofluid or tissue, has proven effective in the rapid identification of radiation biomarkers and metabolic perturbations. To investigate how the genotypic background may alter the ionizing radiation (IR) signature, we analyzed urine from Parp1{sup −/−} mice, as a model radiosensitive genotype, exposed to IR by utilizing the analytical power of liquid chromatography coupled with mass spectrometry (LC–MS), as urine has been thoroughly investigated in wild type (WT) mice in previous studies from our laboratory. Samples were collected at days one and three after irradiation, time points that are important for the early and efficient triage of exposed individuals. Time-dependent perturbations in metabolites were observed in the tricarboxylic acid pathway (TCA). Other differentially excreted metabolites included amino acids and metabolites associated with dysregulation of energy metabolism pathways. Time-dependent apoptotic pathway activation between WT and mutant mice following IR exposure may explain the

Metabolomics: Definitions and Significance in Systems Biology.

Science.gov (United States)

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.

Metabolomics to Explore Impact of Dairy Intake

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Hong Zheng

2015-06-01

Full Text Available Dairy products are an important component in the Western diet and represent a valuable source of nutrients for humans. However, a reliable dairy intake assessment in nutrition research is crucial to correctly elucidate the link between dairy intake and human health. Metabolomics is considered a potential tool for assessment of dietary intake instead of traditional methods, such as food frequency questionnaires, food records, and 24-h recalls. Metabolomics has been successfully applied to discriminate between consumption of different dairy products under different experimental conditions. Moreover, potential metabolites related to dairy intake were identified, although these metabolites need to be further validated in other intervention studies before they can be used as valid biomarkers of dairy consumption. Therefore, this review provides an overview of metabolomics for assessment of dairy intake in order to better clarify the role of dairy products in human nutrition and health.

Cluster abundance in chameleon f ( R ) gravity I: toward an accurate halo mass function prediction

Energy Technology Data Exchange (ETDEWEB)

Cataneo, Matteo; Rapetti, David [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen (Denmark); Lombriser, Lucas [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); Li, Baojiu, E-mail: matteoc@dark-cosmology.dk, E-mail: drapetti@dark-cosmology.dk, E-mail: llo@roe.ac.uk, E-mail: baojiu.li@durham.ac.uk [Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham DH1 3LE (United Kingdom)

2016-12-01

We refine the mass and environment dependent spherical collapse model of chameleon f ( R ) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N -body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f ( R ) halo abundance with respect to that of General Relativity (GR) within a precision of ∼< 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f ( R ) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

Identification and accurate quantification of structurally related peptide impurities in synthetic human C-peptide by liquid chromatography-high resolution mass spectrometry.

Science.gov (United States)

Li, Ming; Josephs, Ralf D; Daireaux, Adeline; Choteau, Tiphaine; Westwood, Steven; Wielgosz, Robert I; Li, Hongmei

2018-06-04

Peptides are an increasingly important group of biomarkers and pharmaceuticals. The accurate purity characterization of peptide calibrators is critical for the development of reference measurement systems for laboratory medicine and quality control of pharmaceuticals. The peptides used for these purposes are increasingly produced through peptide synthesis. Various approaches (for example mass balance, amino acid analysis, qNMR, and nitrogen determination) can be applied to accurately value assign the purity of peptide calibrators. However, all purity assessment approaches require a correction for structurally related peptide impurities in order to avoid biases. Liquid chromatography coupled to high resolution mass spectrometry (LC-hrMS) has become the key technique for the identification and accurate quantification of structurally related peptide impurities in intact peptide calibrator materials. In this study, LC-hrMS-based methods were developed and validated in-house for the identification and quantification of structurally related peptide impurities in a synthetic human C-peptide (hCP) material, which served as a study material for an international comparison looking at the competencies of laboratories to perform peptide purity mass fraction assignments. More than 65 impurities were identified, confirmed, and accurately quantified by using LC-hrMS. The total mass fraction of all structurally related peptide impurities in the hCP study material was estimated to be 83.3 mg/g with an associated expanded uncertainty of 3.0 mg/g (k = 2). The calibration hierarchy concept used for the quantification of individual impurities is described in detail. Graphical abstract ᅟ.

GC-MS Based Plasma Metabolomics for Identification of Candidate Biomarkers for Hepatocellular Carcinoma in Egyptian Cohort.

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Mohammad R Nezami Ranjbar

Full Text Available This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS. Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS. Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC.

Metabolomics studies in brain tissue: A review.

Science.gov (United States)

Gonzalez-Riano, Carolina; Garcia, Antonia; Barbas, Coral

2016-10-25

Brain is still an organ with a composition to be discovered but beyond that, mental disorders and especially all diseases that curse with dementia are devastating for the patient, the family and the society. Metabolomics can offer an alternative tool for unveiling new insights in the discovery of new treatments and biomarkers of mental disorders. Until now, most of metabolomic studies have been based on biofluids: serum/plasma or urine, because brain tissue accessibility is limited to animal models or post mortem studies, but even so it is crucial for understanding the pathological processes. Metabolomics studies of brain tissue imply several challenges due to sample extraction, along with brain heterogeneity, sample storage, and sample treatment for a wide coverage of metabolites with a wide range of concentrations of many lipophilic and some polar compounds. In this review, the current analytical practices for target and non-targeted metabolomics are described and discussed with emphasis on critical aspects: sample treatment (quenching, homogenization, filtration, centrifugation and extraction), analytical methods, as well as findings considering the used strategies. Besides that, the altered analytes in the different brain regions have been associated with their corresponding pathways to obtain a global overview of their dysregulation, trying to establish the link between altered biological pathways and pathophysiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Specific metabolomics adaptations define a differential regional vulnerability in the adult human cerebral cortex

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Rosanna Cabré

2016-12-01

Full Text Available Brain neurons offer diverse responses to stresses and detrimental factors during development and aging, and as a result of both neurodegenerative and neuropsychiatric disorders. This multiplicity of responses can be ascribed to the great diversity among neuronal populations. Here we have determined the metabolomic profile of three healthy adult human brain regions—entorhinal cortex, hippocampus, and frontal cortex—using mass spectrometry-based technologies. Our results show the existence of a lessened energy demand, mitochondrial stress, and lower one-carbon metabolism (particularly restricted to the methionine cycle specifically in frontal cortex. These findings, along with the better antioxidant capacity and lower mTOR signaling also seen in frontal cortex, suggest that this brain region is especially resistant to stress compared to the entorhinal cortex and hippocampus, which are more vulnerable regions. Globally, our results show the presence of specific metabolomics adaptations in three mature, healthy human brain regions, confirming the existence of cross-regional differences in cell vulnerability in the human cerebral cortex.

Poplar trees reconfigure the transcriptome and metabolome in response to drought in a genotype- and time-of-day-dependent manner.

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Hamanishi, Erin T; Barchet, Genoa L H; Dauwe, Rebecca; Mansfield, Shawn D; Campbell, Malcolm M

2015-04-21

Drought has a major impact on tree growth and survival. Understanding tree responses to this stress can have important application in both conservation of forest health, and in production forestry. Trees of the genus Populus provide an excellent opportunity to explore the mechanistic underpinnings of forest tree drought responses, given the growing molecular resources that are available for this taxon. Here, foliar tissue of six water-deficit stressed P. balsamifera genotypes was analysed for variation in the metabolome in response to drought and time of day by using an untargeted metabolite profiling technique, gas chromatography/mass-spectrometry (GC/MS). Significant variation in the metabolome was observed in response the imposition of water-deficit stress. Notably, organic acid intermediates such as succinic and malic acid had lower concentrations in leaves exposed to drought, whereas galactinol and raffinose were found in increased concentrations. A number of metabolites with significant difference in accumulation under water-deficit conditions exhibited intraspecific variation in metabolite accumulation. Large magnitude fold-change accumulation was observed in three of the six genotypes. In order to understand the interaction between the transcriptome and metabolome, an integrated analysis of the drought-responsive transcriptome and the metabolome was performed. One P. balsamifera genotype, AP-1006, demonstrated a lack of congruence between the magnitude of the drought transcriptome response and the magnitude of the metabolome response. More specifically, metabolite profiles in AP-1006 demonstrated the smallest changes in response to water-deficit conditions. Pathway analysis of the transcriptome and metabolome revealed specific genotypic responses with respect to primary sugar accumulation, citric acid metabolism, and raffinose family oligosaccharide biosynthesis. The intraspecific variation in the molecular strategies that underpin the responses to drought

Gas Chromatography/Mass Spectrometry-Based Metabolomic Profiling Reveals Alterations in Mouse Plasma and Liver in Response to Fava Beans.

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Man Xiao

Full Text Available Favism is a life-threatening hemolytic anemia resulting from the intake of fava beans by susceptible individuals with low erythrocytic glucose 6-phosphate dehydrogenase (G6PD activity. However, little is known about the metabolomic changes in plasma and liver after the intake of fava beans in G6PD normal and deficient states. In this study, gas chromatography/mass spectrometry was used to analyze the plasma and liver metabolic alterations underlying the effects of fava beans in C3H- and G6PD-deficient (G6PDx mice, and to find potential biomarkers and metabolic changes associated with favism. Our results showed that fava beans induced oxidative stress in both C3H and G6PDx mice. Significantly, metabolomic differences were observed in plasma and liver between the control and fava bean treated groups of both C3H and G6PDx mice. The levels of 7 and 21 metabolites in plasma showed significant differences between C3H-control (C3H-C- and C3H fava beans-treated (C3H-FB mice, and G6PDx-control (G6PDx-C- and G6PDx fava beans-treated (G6PDx-FB mice, respectively. Similarly, the levels of 7 and 25 metabolites in the liver showed significant differences between C3H and C3H-FB, and G6PDx and G6PDx-FB, respectively. The levels of oleic acid, linoleic acid, and creatinine were significantly increased in the plasma of both C3H-FB and G6PDx-FB mice. In the liver, more metabolic alterations were observed in G6PDx-FB mice than in C3H-FB mice, and were involved in a sugar, fatty acids, amino acids, cholesterol biosynthesis, the urea cycle, and the nucleotide metabolic pathway. These findings suggest that oleic acid, linoleic acid, and creatinine may be potential biomarkers of the response to fava beans in C3H and G6PDx mice and therefore that oleic acid and linoleic acid may be involved in oxidative stress induced by fava beans. This study demonstrates that G6PD activity in mice can affect their metabolic pathways in response to fava beans.

Gas Chromatography/Mass Spectrometry-Based Metabolomic Profiling Reveals Alterations in Mouse Plasma and Liver in Response to Fava Beans.

Science.gov (United States)

Xiao, Man; Du, Guankui; Zhong, Guobing; Yan, Dongjing; Zeng, Huazong; Cai, Wangwei

2016-01-01

Favism is a life-threatening hemolytic anemia resulting from the intake of fava beans by susceptible individuals with low erythrocytic glucose 6-phosphate dehydrogenase (G6PD) activity. However, little is known about the metabolomic changes in plasma and liver after the intake of fava beans in G6PD normal and deficient states. In this study, gas chromatography/mass spectrometry was used to analyze the plasma and liver metabolic alterations underlying the effects of fava beans in C3H- and G6PD-deficient (G6PDx) mice, and to find potential biomarkers and metabolic changes associated with favism. Our results showed that fava beans induced oxidative stress in both C3H and G6PDx mice. Significantly, metabolomic differences were observed in plasma and liver between the control and fava bean treated groups of both C3H and G6PDx mice. The levels of 7 and 21 metabolites in plasma showed significant differences between C3H-control (C3H-C)- and C3H fava beans-treated (C3H-FB) mice, and G6PDx-control (G6PDx-C)- and G6PDx fava beans-treated (G6PDx-FB) mice, respectively. Similarly, the levels of 7 and 25 metabolites in the liver showed significant differences between C3H and C3H-FB, and G6PDx and G6PDx-FB, respectively. The levels of oleic acid, linoleic acid, and creatinine were significantly increased in the plasma of both C3H-FB and G6PDx-FB mice. In the liver, more metabolic alterations were observed in G6PDx-FB mice than in C3H-FB mice, and were involved in a sugar, fatty acids, amino acids, cholesterol biosynthesis, the urea cycle, and the nucleotide metabolic pathway. These findings suggest that oleic acid, linoleic acid, and creatinine may be potential biomarkers of the response to fava beans in C3H and G6PDx mice and therefore that oleic acid and linoleic acid may be involved in oxidative stress induced by fava beans. This study demonstrates that G6PD activity in mice can affect their metabolic pathways in response to fava beans.

Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity

International Nuclear Information System (INIS)

Beger, Richard D.; Sun, Jinchun; Schnackenberg, Laura K.

2010-01-01

Hepatotoxicity and nephrotoxicity are two major reasons that drugs are withdrawn post-market, and hence it is of major concern to both the FDA and pharmaceutical companies. The number of cases of serious adverse effects (SAEs) in marketed drugs has climbed faster than the number of total drug prescriptions issued. In some cases, preclinical animal studies fail to identify the potential toxicity of a new chemical entity (NCE) under development. The current clinical chemistry biomarkers of liver and kidney injury are inadequate in terms of sensitivity and/or specificity, prompting the need to discover new translational specific biomarkers of organ injury. Metabolomics along with genomics and proteomics technologies have the capability of providing translational diagnostic and prognostic biomarkers specific for early stages of liver and kidney injury. Metabolomics has several advantages over the other omics platforms such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in preclinical and clinical studies. The metabolomics platform is reviewed with particular emphasis on applications involving drug-induced hepatotoxicity and nephrotoxicity. Analytical platforms for metabolomics, chemometrics for mining metabolomics data and the applications of the metabolomics technologies are covered in detail with emphasis on recent work in the field.

Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review

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Background: Metabolomics is a promising tool of cardiovascular biomarker discovery. We systematically reviewed the literature on comprehensive metabolomic profiling in association with incident cardiovascular disease (CVD). Methods and Results: We searched MEDLINE and EMBASE from inception to Janua...

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.

Science.gov (United States)

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.

Liquid–liquid extraction combined with differential isotope dimethylaminophenacyl labeling for improved metabolomic profiling of organic acids

International Nuclear Information System (INIS)

Peng, Jun; Li, Liang

2013-01-01

Graphical abstract: -- Highlights: •An improved method for profiling the carboxylic acid sub-metabolome is reported. •Liquid–liquid extraction was used for separating the organic acids from the amines. • 12 C/ 13 C-p-dimethylaminophenacyl (DmPA) labeling of the organic acids was carried out on the extract. •Detection interference by amines and labeling efficiency reduction by water were reduced. •About 2500 12 C/ 13 C-peak pairs or putative metabolites could be detected from 20 μL of human urine. -- Abstract: A large fraction of the known human metabolome belong to organic acids. However, comprehensive profiling of the organic acid sub-metabolome is a major analytical challenge. In this work, we report an improved method for detecting organic acid metabolites. This method is based on the use of liquid–liquid extraction (LLE) to selectively extract the organic acids, followed by using differential isotope p-dimethylaminophenacyl (DmPA) labeling of the acid metabolites. The 12 C-/ 13 C-labeled samples are analyzed by liquid chromatography Fourier-transform ion cyclotron resonance mass spectrometry (LC–FTICR–MS). It is shown that this LLE DmPA labeling method offers superior performance over the method of direct DmPA labeling of biofluids such as human urine. LLE of organic acids reduces the interference of amine-containing metabolites that may also react with DmPA. It can also remove water in a biofluid that can reduce the labeling efficiency. Using human urine as an example, it is demonstrated that about 2500 peak pairs or putative metabolites could be detected in a 30-min gradient LC–MS run, which is about 3 times more than that detected in a sample prepared using direct DmPA labeling. About 95% of the 1000 or so matched metabolites to the Human Metabolome Database (HMDB) are organic acids. It is further shown that this method can be used to handle as small as 10 μL of urine. We believe that this method opens the possibility of generating a

Liquid–liquid extraction combined with differential isotope dimethylaminophenacyl labeling for improved metabolomic profiling of organic acids

Energy Technology Data Exchange (ETDEWEB)

Peng, Jun; Li, Liang, E-mail: Liang.Li@ualberta.ca

2013-11-25

Graphical abstract: -- Highlights: •An improved method for profiling the carboxylic acid sub-metabolome is reported. •Liquid–liquid extraction was used for separating the organic acids from the amines. •{sup 12}C/{sup 13}C-p-dimethylaminophenacyl (DmPA) labeling of the organic acids was carried out on the extract. •Detection interference by amines and labeling efficiency reduction by water were reduced. •About 2500 {sup 12}C/{sup 13}C-peak pairs or putative metabolites could be detected from 20 μL of human urine. -- Abstract: A large fraction of the known human metabolome belong to organic acids. However, comprehensive profiling of the organic acid sub-metabolome is a major analytical challenge. In this work, we report an improved method for detecting organic acid metabolites. This method is based on the use of liquid–liquid extraction (LLE) to selectively extract the organic acids, followed by using differential isotope p-dimethylaminophenacyl (DmPA) labeling of the acid metabolites. The {sup 12}C-/{sup 13}C-labeled samples are analyzed by liquid chromatography Fourier-transform ion cyclotron resonance mass spectrometry (LC–FTICR–MS). It is shown that this LLE DmPA labeling method offers superior performance over the method of direct DmPA labeling of biofluids such as human urine. LLE of organic acids reduces the interference of amine-containing metabolites that may also react with DmPA. It can also remove water in a biofluid that can reduce the labeling efficiency. Using human urine as an example, it is demonstrated that about 2500 peak pairs or putative metabolites could be detected in a 30-min gradient LC–MS run, which is about 3 times more than that detected in a sample prepared using direct DmPA labeling. About 95% of the 1000 or so matched metabolites to the Human Metabolome Database (HMDB) are organic acids. It is further shown that this method can be used to handle as small as 10 μL of urine. We believe that this method opens the

Targeted metabolomics profiles are strongly correlated with nutritional patterns in women

OpenAIRE

Menni, Cristina; Zhai, Guangju; MacGregor, Alexander; Prehn, Cornelia; Römisch-Margl, Werner; Suhre, Karsten; Adamski, Jerzy; Cassidy, Aedin; Illig, Thomas; Spector, Tim D.; Valdes, Ana M.

2013-01-01

Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targete...

Accurate prediction of the ammonia probes of a variable proton-to-electron mass ratio

Science.gov (United States)

Owens, A.; Yurchenko, S. N.; Thiel, W.; Špirko, V.

2015-07-01

A comprehensive study of the mass sensitivity of the vibration-rotation-inversion transitions of 14NH3, 15NH3, 14ND3 and 15ND3 is carried out variationally using the TROVE approach. Variational calculations are robust and accurate, offering a new way to compute sensitivity coefficients. Particular attention is paid to the Δk = ±3 transitions between the accidentally coinciding rotation-inversion energy levels of the ν2 = 0+, 0-, 1+ and 1- states, and the inversion transitions in the ν4 = 1 state affected by the `giant' l-type doubling effect. These transitions exhibit highly anomalous sensitivities, thus appearing as promising probes of a possible cosmological variation of the proton-to-electron mass ratio μ. Moreover, a simultaneous comparison of the calculated sensitivities reveals a sizeable isotopic dependence which could aid an exclusive ammonia detection.

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

NARCIS (Netherlands)

Salek, R.M.; Neumann, S.; Schober, D.; Hummel, J.; Billiau, K.; Kopka, J.; Correa, E.; Reijmers, T.; Rosato, A.; Tenori, L.; Turano, P.; Marin, S.; Deborde, C.; Jacob, D.; Rolin, D.; Dartigues, B.; Conesa, P.; Haug, K.; Rocca-Serra, P.; O’Hagan, S.; Hao, J.; Vliet, M. van; Sysi-Aho, M.; Ludwig, C.; Bouwman, J.; Cascante, M.; Ebbels, T.; Griffin, J.L.; Moing, A.; Nikolski, M.; Oresic, M.; Sansone, S.A.; Viant, M.R.; Goodacre, R.; Günther, U.L.; Hankemeier, T.; Luchinat, C.; Walther, D.; Steinbeck, C.

2015-01-01

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

AICAR stimulation metabolome widely mimics electrical contraction in isolated rat epitrochlearis muscle.

Science.gov (United States)

Miyamoto, Licht; Egawa, Tatsuro; Oshima, Rieko; Kurogi, Eriko; Tomida, Yosuke; Tsuchiya, Koichiro; Hayashi, Tatsuya

2013-12-15

Physical exercise has potent therapeutic and preventive effects against metabolic disorders. A number of studies have suggested that 5'-AMP-activated protein kinase (AMPK) plays a pivotal role in regulating carbohydrate and lipid metabolism in contracting skeletal muscles, while several genetically manipulated animal models revealed the significance of AMPK-independent pathways. To elucidate significance of AMPK and AMPK-independent signals in contracting skeletal muscles, we conducted a metabolomic analysis that compared the metabolic effects of 5-aminoimidazole-4-carboxamide-1-β-D-ribonucleoside (AICAR) stimulation with the electrical contraction ex vivo in isolated rat epitrochlearis muscles, in which both α1- and α2-isoforms of AMPK and glucose uptake were equally activated. The metabolomic analysis using capillary electrophoresis time-of-flight mass spectrometry detected 184 peaks and successfully annotated 132 small molecules. AICAR stimulation exhibited high similarity to the electrical contraction in overall metabolites. Principal component analysis (PCA) demonstrated that the major principal component characterized common effects whereas the minor principal component distinguished the difference. PCA and a factor analysis suggested a substantial change in redox status as a result of AMPK activation. We also found a decrease in reduced glutathione levels in both AICAR-stimulated and contracting muscles. The muscle contraction-evoked influences related to the metabolism of amino acids, in particular, aspartate, alanine, or lysine, are supposed to be independent of AMPK activation. Our results substantiate the significance of AMPK activation in contracting skeletal muscles and provide novel evidence that AICAR stimulation closely mimics the metabolomic changes in the contracting skeletal muscles.

Metabolomics techniques for nanotoxicity investigations.

Science.gov (United States)

Lv, Mengying; Huang, Wanqiu; Chen, Zhipeng; Jiang, Hulin; Chen, Jiaqing; Tian, Yuan; Zhang, Zunjian; Xu, Fengguo

2015-01-01

Nanomaterials are commonly defined as engineered structures with at least one dimension of 100 nm or less. Investigations of their potential toxicological impact on biological systems and the environment have yet to catch up with the rapid development of nanotechnology and extensive production of nanoparticles. High-throughput methods are necessary to assess the potential toxicity of nanoparticles. The omics techniques are well suited to evaluate toxicity in both in vitro and in vivo systems. Besides genomic, transcriptomic and proteomic profiling, metabolomics holds great promises for globally evaluating and understanding the molecular mechanism of nanoparticle-organism interaction. This manuscript presents a general overview of metabolomics techniques, summarizes its early application in nanotoxicology and finally discusses opportunities and challenges faced in nanotoxicology.

Evolution of potent odorants within the volatile metabolome of high-quality hazelnuts (Corylus avellana L.): evaluation by comprehensive two-dimensional gas chromatography coupled with mass spectrometry.

Science.gov (United States)

Rosso, Marta Cialiè; Liberto, Erica; Spigolon, Nicola; Fontana, Mauro; Somenzi, Marco; Bicchi, Carlo; Cordero, Chiara

2018-01-09

Within the pattern of volatiles released by food products (volatilome), potent odorants are bio-active compounds that trigger aroma perception by activating a complex array of odor receptors (ORs) in the regio olfactoria. Their informative role is fundamental to select optimal post-harvest and storage conditions and preserve food sensory quality. This study addresses the volatile metabolome from high-quality hazelnuts (Corylus avellana L.) from the Ordu region (Turkey) and Tonda Romana from Italy, and investigates its evolution throughout the production chain (post-harvest, industrial storage, roasting) to find functional correlations between technological strategies and product quality. The volatile metabolome is analyzed by headspace solid-phase microextration combined with comprehensive two-dimensional gas chromatography and mass spectrometry. Dedicated pattern recognition, based on 2D data (targeted fingerprinting), is used to mine analytical outputs, while principal component analysis (PCA), Fisher ratio, hierarchical clustering, and analysis of variance are used to find decision makers among the most informative chemicals. Low-temperature drying (18-20 °C) has a decisive effect on quality; it correlates negatively with bacteria and mold metabolic activity, nut viability, and lipid oxidation products (2-methyl-1-propanol, 3-methyl-1-butanol, 2-ethyl-1-hexanol, 2-octanol, 1-octen-3-ol, hexanal, octanal and (E)-2-heptanal). Protective atmosphere storage (99% N 2 -1% O 2 ) effectively limits lipid oxidation for 9-12 months after nut harvest. The combination of optimal drying and storage preserves the aroma potential; after roasting at different shelf-lives, key odorants responsible for malty and buttery (2- and 3-methylbutanal, 2,3-butanedione and 2,3-pentanedione), earthy (methylpyrazine, 2-ethyl-5-methyl pyrazine and 3-ethyl-2,5-dimethyl pyrazine) and caramel-like and musty notes (2,5-dimethyl-4-hydroxy-3(2H)-furanone - furaneol and acetyl pyrrole) show no

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

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Jianwen She

2013-09-01

Full Text Available Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC, reversed-phase liquid chromatography (RP–LC, and gas chromatography (GC. All three techniques are coupled to a mass spectrometer (MS in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

Metabolomic biosignature differentiates melancholic depressive patients from healthy controls.

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Liu, Yashu; Yieh, Lynn; Yang, Tao; Drinkenburg, Wilhelmus; Peeters, Pieter; Steckler, Thomas; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

2016-08-23

Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are "melancholic depression" and "anxious depression". Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression. With the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress. We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted

Metabolomic NMR fingerprinting: an exploratory and predictive tool

OpenAIRE

Lauri, Ilaria

2014-01-01

Metabolomics is the comprehensive assessment of low molecular weight organic metabolites within biological system. The identification and characterization of several chemical species, or metabolic fingerprinting, is an emergent approach in metabolomics field that provides a valuable “snapshot” of metabolic profiles. This approach is finding an increasing number of applications in many areas including cancer research, drug discovery and food science. The combined use of NMR spectroscopy, data ...

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

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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.

Biomarker Identification and Pathway Analysis by Serum Metabolomics of Lung Cancer

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Yingrong Chen

2015-01-01

Full Text Available Lung cancer is one of the most common causes of cancer death, for which no validated tumor biomarker is sufficiently accurate to be useful for diagnosis. Additionally, the metabolic alterations associated with the disease are unclear. In this study, we investigated the construction, interaction, and pathways of potential lung cancer biomarkers using metabolomics pathway analysis based on the Kyoto Encyclopedia of Genes and Genomes database and the Human Metabolome Database to identify the top altered pathways for analysis and visualization. We constructed a diagnostic model using potential serum biomarkers from patients with lung cancer. We assessed their specificity and sensitivity according to the area under the curve of the receiver operator characteristic (ROC curves, which could be used to distinguish patients with lung cancer from normal subjects. The pathway analysis indicated that sphingolipid metabolism was the top altered pathway in lung cancer. ROC curve analysis indicated that glycerophospho-N-arachidonoyl ethanolamine (GpAEA and sphingosine were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. Compared with the traditional lung cancer diagnostic biomarkers carcinoembryonic antigen and cytokeratin 19 fragment, GpAEA and sphingosine were as good or more appropriate for detecting lung cancer. We report our identification of potential metabolic diagnostic and prognostic biomarkers of lung cancer and clarify the metabolic alterations in lung cancer.

The future of metabolomics in ELIXIR [version 1; referees: 2 approved, 1 approved with reservations

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Merlijn van Rijswijk

2017-09-01

Full Text Available 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.

The future of metabolomics in ELIXIR [version 2; referees: 2 approved, 1 approved with reservations