Metabolite identification via the Madison Metabolomics Consortium Database (original) (raw)
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F1000 - Post-publication peer review of the biomedical literature
A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to E. coli extract where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures overcoming the need of experimental MS and NMR metabolite databases.
Background: One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research. Results: We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter’s metabolite identification methods. Conclusions: MetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries. Availability: http://www.nrcbioinformatics.ca/metabohunter/
Metabolite Identification in NMR-based Metabolomics
Current Metabolomics, 2015
To achieve goals in metabolomics investigations, it is necessary to produce a comprehensive metabolic profiling from biological samples. Identification of metabolites is one of the important steps in metabolomics studies and the conclusion drawn from such studies depends on how exactly the metabolites are identified. NMR is one of the most selective analytical techniques which offers structural information of molecules. But, due to complex biological sample matrix, metabolic identification step needs application of advanced NMR techniques and analytical strategies for better accuracy. This review covers the analytical methods and strategies used for identification of metabolites in NMR-based metabolomics. The specific problems and troubleshoots associated with identification of metabolites in biological samples are discussed in details.
Metabolomics
Public databases of NMR spectra of low molecular weight metabolites must be constructed to remove the major bottleneck of metabolite identification and quantification in the analysis of metabolomics data. Two-dimensional (2-D) 1H J-resolved spectroscopy represents a popular alternative to 1-D NMR methods, resolving the highly overlapped signals characteristic of complex metabolite mixtures across two frequency dimensions. Here we report the design, measurement and curation of, primarily, a database of 2-D J-resolved NMR spectra. Metabolites were selected based upon their importance within metabolic pathways and their detection potential by NMR, and prepared for analysis at pH 6.6, 7.0 and 7.4. Sixteen NMR spectra were recorded for each metabolite using a 500 MHz spectrometer, including 1-D and 2-D J-resolved spectra, different water suppression methods and different acquisition parameters. Some metabolites were removed due to limited solubility, poor NMR signal quality or contamination, and the final dataset comprised of 3328 NMR spectra arising from 208 metabolite standards. These data are housed in a purpose-built MySQL database (Birmingham Metabolite Library; BML-NMR) containing over 100 separate tables and allowing the efficient storage of raw free-induction-decays (FIDs), 1-D and 2-D NMR spectra and associated metadata. The database is compliant with the Metabolomics Standards Initiative (MSI) endorsed reporting requirements, with some necessary amendments. Library data can be accessed freely and searched through a custom written web interface (www.bml-nmr.org). FIDs, NMR spectra and associated metadata can be downloaded according to a newly implemented MSI-compatible XML schema.
Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction
Metabolites, 2018
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 . 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.
Databases and Software for NMR-Based Metabolomics
Current Metabolomics, 2012
New software and increasingly sophisticated NMR metabolite spectral databases are advancing the unique abilities of NMR spectroscopy to identify and quantify small molecules in solution for studies of metabolite biomarkers and metabolic flux. Public and commercial databases now contain experimental 1D 1 H, 13 C and 2D 1 H-13 C spectra and extracted spectral parameters for over a thousand compounds and theoretical data for thousands more. Public databases containing experimental NMR data from complex metabolic studies are emerging. These databases are providing information vital for the construction and testing of new computational algorithms for NMR-based chemometric and quantitative metabolomics studies. In this review we focus on database and software tools that support a quantitative NMR approach to the analysis of 1D and 2D NMR spectra of complex biological mixtures.
Advances in NMR and MS-based Metabolomics
2017
Mass spectrometry, almost always coupled with chromatographic separation, is one of the techniques of election able to perform a comprehensive investigation of the "metabolome". MS is almost "universal" and characteristics like sensitivity and dynamic range make it an ideal tool for this task. For exactly the same reasons, however, the results of a MS-based metabolomic investigation are sensitive to any issue occurring during sample preparation, sample analysis and data preprocessing. Errors occurring in these phases will affect all the downstream statistical analysis following the well known "garbage in, garbage out" principle. The aim of this tutorial is to highlight the most critical aspect which should be taken into consideration when designing a (successful!) MS-based metabolomics assay. The discussion will touch lab practice, quality assessment and data preprocessing. The objective is to describe the strategies which can be used to control the maj...
Metabolomics, 2017
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 1 H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.
Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics
Progress in nuclear magnetic resonance spectroscopy, 2017
Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (⩾1μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼10(3)-10(4)) and dynamic range (∼10(3)-10(4)), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas ...