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1.
Rachel A. Spicer Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):16
Introduction
Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.Objectives
(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.Methods
A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.Results
Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.Conclusion
Further efforts are required to improve data sharing in metabolomics.2.
Introduction
Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.Objectives
Assessment of the quality of raw data processing in untargeted metabolomics.Methods
Five published untargeted metabolomics studies, were reanalyzed.Results
Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.Conclusion
Incomplete raw data processing shows unexplored potential of current and legacy data.3.
Antonio Rosato Leonardo Tenori Marta Cascante Pedro Ramon De Atauri Carulla Vitor A. P. Martins dos Santos Edoardo Saccenti 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):37
Introduction
Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.Objectives
This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.Methods
We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.Results
We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.Conclusions
Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.4.
Elliott J. Price Ranjana Bhattacharjee Antonio Lopez-Montes Paul D. Fraser 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):144
Introduction
Ninety-seven percent of yam (Dioscorea spp.) production takes place in low income food deficit countries (LIFDCs) and the crop provides 200 calories a day to approximately 300 million people. Therefore, yams are vital for food security. Yams have high-yield potential and high market value potential yet current breeding of yam is hindered by a lack of genomic information and genetic resources. New tools are needed to modernise breeding strategies and unlock the potential of yam to improve livelihood in LIFDCs.Objectives
Metabolomic screening has been undertaken on a diverse panel of Dioscorea accessions to assess the utility of the approach for advancing breeding strategies in this understudied crop.Methods
Polar and lipophilic extracts from tubers of accessions from the global yam breeding program have been comprehensively profiled via gas chromatography-mass spectrometry.Results
A visual pathway representation of the measured yam tuber metabolome has been delivered as a resource for biochemical evaluation of yam germplasm. Over 200 compounds were routinely measured in tubers, providing a major advance for the chemo-typing of this crop. Core biochemical redundancy concealed trends that were only elucidated following detailed mining of global metabolomics data. Combined analysis on leaf and tuber material identified a subset of metabolites which allow accurate species classification and highlighted the potential of predicting tuber composition from leaf profiles. Metabolic variation was accession-specific and often localised to compound classes, which will aid trait-targeting for metabolite markers.Conclusions
Metabolomics provides a standalone platform with potential to deliver near-future crop gains for yam. The approach compliments the genetic advancements currently underway and integration with other ‘–omics’ studies will deliver a significant advancement to yam breeding strategies.5.
Sonia Liggi Christine Hinz Zoe Hall Maria Laura Santoru Simone Poddighe John Fjeldsted Luigi Atzori Julian L. Griffin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):52
Introduction
Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives
Merge in the same platform the steps required for metabolomics data processing.Methods
KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results
The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion
KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.6.
7.
Lia Bally Cédric Bovet Christos T. Nakas Thomas Zueger Jean-Christophe Prost Jean-Marc Nuoffer Alexander B. Leichtle Georg Martin Fiedler Christoph Stettler 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):78
Introduction
Exercise-associated metabolism in type 1 diabetes (T1D) remains under-studied due to the complex interplay between exogenous insulin, counter-regulatory hormones and insulin-sensitivity.Objective
To identify the metabolic differences induced by two exercise modalities in T1D using ultra high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC–HRMS) based metabolomics.Methods
Twelve T1D adults performed intermittent high-intensity (IHE) and continuous-moderate-intensity (CONT) exercise. Serum samples were analysed by UHPLC–HRMS.Results
Metabolic profiling of IHE and CONT highlighted exercise-induced changes in purine and acylcarnitine metabolism.Conclusion
IHE may increase beta-oxidation through higher ATP-turnover. UHPLC–HRMS based metabolomics as a data-driven approach without an a priori hypothesis may help uncover distinctive metabolic effects during exercise in T1D.Clinical trial registration number is www.clinicaltrials.gov: NCT02068638.8.
9.
Alžběta Gardlo Age K. Smilde Karel Hron Marcela Hrdá Radana Karlíková David Friedecký Tomáš Adam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(7):117
Introduction
One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.Objectives
Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.Methods
In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.Results
We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.Conclusion
For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.10.
D. Jacob C. Deborde M. Lefebvre M. Maucourt A. Moing 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):36
Introduction
Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.Objectives
The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.Methods
NMRProcFlow was developed to be a graphical and interactive 1D NMR (1H & 13C) spectra processing tool.Results
NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.Conclusion
Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.11.
Caroline Muschet Gabriele Möller Cornelia Prehn Martin Hrabě de Angelis Jerzy Adamski Janina Tokarz 《Metabolomics : Official journal of the Metabolomic Society》2016,12(10):151
Introduction
Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.Objectives
We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.Methods
We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.Results
We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.Conclusion
We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.12.
Yanhui Ge Mengmeng Sun Luis F. Salomé-Abarca Mei Wang Young Hae Choi 《Metabolomics : Official journal of the Metabolomic Society》2018,14(10):137
Introduction
The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.Objectives
Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting 1H nuclear magnetic resonance (1H NMR) spectroscopy.Method
The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by 1H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.Results
The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.Conclusion
These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.13.
Anita H. Lewin Peter Silinski James Hayes Amanda Gilbert S. Wayne Mascarella Herbert H. Seltzman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):117
Introduction
Metabolomics analysis depends on the identification and validation of specific metabolites. This task is significantly hampered by the absence of well-characterized reference standards. The one-carbon carrier 10-formyltetrahydrofolate acts as a donor of formyl groups in anabolism, where it is a substrate in formyltransferase reactions in purine biosynthesis. It has been reported as an unstable substance and is currently unavailable as a reference standard for metabolomics analysis.Objectives
The current study was undertaken to provide the metabolomics community thoroughly characterized 10-formyltetrahydrofolate along with analytical methodology and guidelines for its storage and handling.Methods
Anaerobic base treatment of 5,10-methenyltetrahydrofolate chloride in the presence of antioxidant was utilized to prepare 10-formyltetrahydrofolate.Results
Pure 10-formyltetrahydrofolate has been prepared and physicochemically characterized. Conditions toward maintaining the stability of a solution of the dipotassium salt of 10-formyltetrahydrofolate have been determined.Conclusion
This study describes the facile preparation of pure (>90%) 10-formyltetrahydrofolate, its qualitative physicochemical characterization, as well as conditions to enable its use as a reference standard in physiologic samples.14.
15.
Jose Manuel Rios-Yuil 《Current fungal infection reports》2017,11(3):92-97
Purpose of Review
Onychomycosis is a growing global health problem. Proper diagnosis is important in order to avoid the risks of unnecessary treatments. This review summarizes the laboratory techniques currently available for the diagnosis of onychomycosis with the advantages and disadvantages of each test.Recent Findings
Research is underway to prove the clinical utility of other diagnostic techniques such as PAS-stained nail biopsies and molecular tests as viable alternatives for the diagnosis of onychomycosis with promissory results.Summary
Dermoscopy can be helpful to differentiate onychomycosis from traumatic onycholysis or true melanonychia; however, a definitive diagnosis requires the demonstration of the presence of the fungi. The combination of direct microscopy using potassium hydroxide and fungal culture is the gold standard for the diagnosis of onychomycosis; however, other alternatives, such as calcofluor white under fluorescence, PAS-stained nail biopsies, and molecular techniques have proven to be excellent methods with their advantages and limitations.16.
Patrick J. C. Tardivel Cécile Canlet Gaëlle Lefort Marie Tremblay-Franco Laurent Debrauwer Didier Concordet Rémi Servien 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):109
Introduction
Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives
In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods
A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.Results
The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion
ASICS is a completely automated procedure to identify and quantify metabolites in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.17.
Normalization and integration of large-scale metabolomics data using support vector regression 总被引:1,自引:0,他引:1
Xiaotao Shen Xiaoyun Gong Yuping Cai Yuan Guo Jia Tu Hao Li Tao Zhang Jialin Wang Fuzhong Xue Zheng-Jiang Zhu 《Metabolomics : Official journal of the Metabolomic Society》2016,12(5):89
Introduction
Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies.Objectives
We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses.Methods
We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.Results
After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected.Conclusion
SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.18.
Effect of gut microbiota on host whole metabolome 总被引:1,自引:0,他引:1
Takeo Moriya Yoshinori Satomi Shumpei Murata Hiroshi Sawada Hiroyuki Kobayashi 《Metabolomics : Official journal of the Metabolomic Society》2017,13(9):101
Introduction
Recent advances in microbiome research have revealed the diverse participation of gut microbiota in a number of diseases. Bacteria-specific endogenous small molecules are produced in the gut, are transported throughout the whole body by circulation, and play key roles in disease establishment. However, the factors and mechanisms underlying these microbial influences largely remain unknown.Objectives
The purpose of this study was to use metabolomics to better understand the influence of microbiota on host physiology.Methods
Germ-free mice (GF) were orally administered with the feces of specific pathogen-free (SPF) mice and were maintained in a vinyl isolator for 4 weeks for establishing the so-called ExGF mice. Comparative metabolomics was performed on luminal contents, feces, urine, plasma, and tissues of GF and ExGF mice.Results
The metabolomics profile of 1716 compounds showed marked difference between GF and ExGF for each matrix. Intestinal differences clearly showed the contribution of microbiota to host digestive activities. In addition, colonic metabolomics revealed the efficient conversion of primary to secondary metabolites by microbiota. Furthermore, metabolomics of tissues and excrements demonstrated the effect of microbiota on the accumulation of metabolites in tissues and during excretion. These effects included known bacterial effects (such as bile acids and amino acids) as well as novel ones, including a drastic decrease of sphingolipids in the host.Conclusion
The diverse effects of microbiota on different sites of the host metabolome were revealed and novel influences on host physiology were demonstrated. These findings should contribute to a deeper understanding of the influence of gut microbiota on disease states and aid in the development of effective intervention strategies.19.
Lai-Fang Zhou Bo-Wen Zhao Ning-Nan Guan Wei-Min Wang Ze-Xia Gao 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):40
Introduction
In some fish species, it is difficult to distinguish mature females from immature females or females that have already spawned via appearance or other convenient methods. Few studies have investigated plasma metabolite profiling for the prediction of fish maturation.Objectives
We investigated the comprehensive metabolic profiles of plasma among immature females and mature females ready to spawn, as well as already spawned breeders of blunt snout bream (Megalobrama amblycephala). The purpose of this study was to screen out potential biomarkers for sexually mature female M. amblycephala compared to immature female individuals and already spawned breeders.Methods
Three groups were set up in this study, which included 1-year-old immature females, 2-year-old sexually mature females ready to spawn and successfully spawned females of M. amblycephala. Plasma samples were collected to investigate comprehensive metabolic profiles through UPLC-MS/MS based on a metabolomics analysis method.Results
According to multivariate and univariate statistical analysis, plasma metabolite profiles of the three groups were clearly separated. The differential plasma metabolites from three hormone related pathways including the GnRH signaling pathway, steroid hormone biosynthesis and steroid biosynthesis, were analyzed. A total of 29 metabolites were identified as differential biomarkers associated with the female maturation status.Conclusion
The identified potential biomarkers could be useful in separating mature M. amblycephala from immature individuals or ovulation-induced female individuals, which would allow for more effective artificial breeding. The results may contribute to a better understanding of the maturation mechanisms of fish in the aspect of metabolomics.20.
Dorothea Lesche Roland Geyer Daniel Lienhard Christos T. Nakas Gaëlle Diserens Peter Vermathen Alexander B. Leichtle 《Metabolomics : Official journal of the Metabolomic Society》2016,12(10):159