Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.
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ABSTRACT:
SUBMITTER: Boiteau RM
PROVIDER: S-EPMC5875998 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Metabolomics plays an important role in phytochemical genomics and crop breeding; however, metabolite annotation is a significant bottleneck in metabolomic studies. In particular, in liquid chromatography-mass spectrometry (MS)-based metabolomics, which has become a routine technology for the profiling of plant-specialized metabolites, a substantial number of metabolites detected as MS peaks are still not assigned properly to a single metabolite. Oryza sativa (rice) is one of the most important staple crops in the world. In the present study, we isolated and elucidated the structures of specialized metabolites from rice by using MS/MS and NMR. Thirty-six compounds, including five new flavonoids and eight rare flavonolignan isomers, were isolated from the rice leaves. The MS/MS spectral data of the isolated compounds, with a detailed interpretation of MS fragmentation data, will facilitate metabolite annotation of the related phytochemicals by enriching the public mass spectral data depositories, including the plant-specific MS/MS-based database, ReSpect.
| S-EPMC4097337 | biostudies-literature
Project description: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 Escherichia 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.
| S-EPMC5035699 | biostudies-literature
Project description:Knowledge of the chemical identity of metabolite molecules is critical for the understanding of the complex biological systems to which they belong. Since metabolite identities and their concentrations are often directly linked to the phenotype, such information can be used to map biochemical pathways and understand their role in health and disease. A very large number of metabolites however are still unknown; i.e., their spectroscopic signatures do not match those in existing databases, suggesting unknown molecule identification is both imperative and challenging. Although metabolites are structurally highly diverse, the majority shares a rather limited number of structural motifs, which are defined by sets of 1H and 13C chemical shifts of the same spin system. This allows one to characterize unknown metabolites by a divide-and-conquer strategy that identifies their structural motifs first. Here, we present the structural motif-based approach "SUMMIT Motif" for the de novo identification of unknown molecular structures in complex mixtures, without the need for extensive purification, using NMR in tandem with two newly curated NMR molecular structural motif metabolomics databases (MSMMDBs). For the identification of structural motif(s), first, the 1H and 13C chemical shifts of all the individual spin systems are extracted from 2D and 3D NMR spectra of the complex mixture. Next, the molecular structural motifs are identified by querying these chemical shifts against the new MSMMDBs. One database, COLMAR MSMMDB, was derived from experimental NMR chemical shifts of known metabolites taken from the COLMAR metabolomics database, while the other MSMMDB, pNMR MSMMDB, is based on predicted chemical shifts of metabolites of several existing large metabolomics databases. For molecules consisting of multiple spin systems, spin systems are connected via long-range scalar J-couplings. When this motif-based identification method was applied to the hydrophilic extract of mouse bile fluid, two unknown metabolites could be successfully identified. This approach is both accurate and efficient for the identification of unknown metabolites and hence enables the discovery of new biochemical processes and potential biomarkers.
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Project description:BACKGROUND: Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. RESULTS: A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. CONCLUSION: NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.
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Project description:Commercial preparations of Ginkgo biloba are very complex mixtures prepared from raw leaf extracts by a series of extraction and prepurification steps. The pharmacological activity is attributed to a number of flavonoid glycosides and unique terpene trilactones (TTLs), with largely uncharacterized pharmacological profiles on targets involved in neurological disorders. It is therefore important to complement existing targeted analytical methods for analysis of Ginkgo biloba preparations with alternative technology platforms for their comprehensive and global characterization. In this work, (1)H NMR-based metabolomics and hyphenation of high-performance liquid chromatography, photo-diode array detection, mass spectrometry, solid-phase extraction, and nuclear magnetic resonance spectroscopy (HPLC-PDA-MS-SPE-NMR) were used for investigation of 16 commercially available preparations of Ginkgo biloba. The standardized extracts originated from Denmark, Italy, Sweden, and United Kingdom, and the results show that (1)H NMR spectra allow simultaneous assessment of the content as well as identity of flavonoid glycosides and TTLs based on a very simple sample-preparation procedure consisting of extraction, evaporation and reconstitution in acetone-d(6). Unexpected or unwanted extract constituents were also easily identified in the (1)H NMR spectra, which contrasts traditional methods that depend on UV absorption or MS ionizability and usually require availability of reference standards. Automated integration of (1)H NMR spectral segments (buckets or bins of 0.02 ppm width) provides relative distribution plots of TTLs based on their H-12 resonances. The present study shows that (1)H NMR-based metabolomics is an attractive method for non-selective and comprehensive analysis of Ginkgo extracts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0195-x) contains supplementary material, which is available to authorized users.
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Project description:Abstract backgroundIsotopic Ratio Outlier Analysis (IROA) is an untargeted metabolomics method that uses stable isotopic labeling and LC-HRMS for identification and relative quantification of metabolites in a biological sample under varying experimental conditions.ObjectiveWe demonstrate a method using high-sensitivity 13C NMR to identify an unknown metabolite isolated from fractionated material from an IROA LC-HRMS experiment.MethodsIROA samples from the nematode Caenorhabditis elegans were fractionated using LC-HRMS using 5 repeated injections and collecting 30 sec fractions. These were concentrated and analyzed by 13C NMR.ResultsWe isotopically labeled samples of C. elegans and collected 2 adjacent LC fractions. By HRMS, one contained at least 2 known metabolites, phenylalanine and inosine, and the other contained tryptophan and an unknown feature with a monoisotopic mass of m/z 380.0742 [M+H]+. With NMR, we were able to easily verify the known compounds, and we then identified the spin system networks responsible for the unknown resonances. After searching the BMRB database and comparing the molecular formula from LC-HRMS, we determined that the fragments were a modified anthranilate and a glucose modified by a phosphate. We then performed quantum chemical NMR chemical shift calculations to determine the most likely isomer, which was 3'-O-phospho-β-D-glucopyranosyl-anthranilate. This compound had previously been found in the same organism, validating our approach.ConclusionWe were able to dereplicate previously known metabolites and identify a metabolite that was not in databases by matching resonances to NMR databases and using chemical shift calculations to determine the correct isomer. This approach is efficient and can be used to identify unknown compounds of interest using the same material used for IROA.
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Project description:Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value.
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Project description:2', 3', 5'-Tri-O-acetyl-N6-(3-hydroxyphenyl) adenosine (also known as WS070117) is a new adenosine analog that displays anti-hyperlipidemic activity both in vitro and in vivo experiments as shown in many preliminary studies. Due to its new structure, little is known about the metabolism of WS070117. Hence, the in vivo metabolites of WS070117 in rat urine following oral administration were investigated. Identification of the metabolites was conducted using the combination of high-performance liquid chromatography (HPLC) coupled with diode array detector (DAD), ion trap electrospray ionization-mass spectrometry (ESI-MS), and off-line microprobe nuclear magnetic resonance (NMR) measurements. Seven metabolites were obtained as pure compounds at the sub-milligram to milligram levels. Results of structure elucidation unambiguously revealed that the phase I metabolite, N6-(3-hydroxyphenyl) adenosine (M8), was a hydrolysate of WS070117 by hydrolysis on the three ester groups. N6-(3-hydr-oxyphenyl) adenine (M7), also one of the phase I metabolites, was the derivative of M8 by the loss of ribofuranose. In addition to two phase I metabolites, there were five phase II metabolites of WS070117 found in rat urine. 8-hydroxy-N6-(3-hydroxy-phenyl) adenosine (M6) was the product of M7 by hydrolysis at position 8. The other four were elucidated to be N6-(3-O-β-D-glucuronyphenyl) adenine (M2), N8-hydroxy-N6-(3-O-sulfophenyl) adenine (M3), N6-(3-O-β-D-glucuronyphenyl) adenosine (M4), and N6-(3-O- sulfophenyl) adenosine (M5). Phase II metabolic pathways were proven to consist of hydroxylation, glucuronidation and sulfation. This study provides new and valuable information on the metabolism of WS070117, and also demonstrates the HPLC/MS/off-line microprobe NMR approach as a robust means for rapid identification of metabolites.
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Project description:Compound identification from accurate mass MS/MS spectra is a bottleneck for untargeted metabolomics. In this study, we propose nine rules of hydrogen rearrangement (HR) during bond cleavages in low-energy collision-induced dissociation (CID). These rules are based on the classic even-electron rule and cover heteroatoms and multistage fragmentation. We evaluated our HR rules by the statistics of MassBank MS/MS spectra in addition to enthalpy calculations, yielding three levels of computational MS/MS annotation: "resolved" (regular HR behavior following HR rules), "semiresolved" (irregular HR behavior), and "formula-assigned" (lacking structure assignment). With this nomenclature, 78.4% of a total of 18506 MS/MS fragment ions in the MassBank database and 84.8% of a total of 36370 MS/MS fragment ions in the GNPS database were (semi-) resolved by predicted bond cleavages. We also introduce the MS-FINDER software for structure elucidation. Molecular formulas of precursor ions are determined from accurate mass, isotope ratio, and product ion information. All isomer structures of the predicted formula are retrieved from metabolome databases, and MS/MS fragmentations are predicted in silico. The structures are ranked by a combined weighting score considering bond dissociation energies, mass accuracies, fragment linkages, and, most importantly, nine HR rules. The program was validated by its ability to correctly calculate molecular formulas with 98.0% accuracy for 5063 MassBank MS/MS records and to yield the correct structural isomer with 82.1% accuracy within the top-3 candidates. In a test with 936 manually identified spectra from an untargeted HILIC-QTOF MS data set of human plasma, formulas were correctly predicted in 90.4% of the cases, and the correct isomer structure was retrieved at 80.4% probability within the top-3 candidates, including for compounds that were absent in mass spectral libraries. The MS-FINDER software is freely available at http://prime.psc.riken.jp/ .
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Project description:Many species from the Pinaceae family have been recognized as a rich source of lignans, flavonoids, and other polyphenolics. The great common occurrence of conifers in Europe, as well as their use in the wood industry, makes both plant material and industrial waste material easily accessible and inexpensive. This is a promising prognosis for both discovery of new active compounds as well as for finding new applications for wood and its industry waste products. This study aimed to analyze and phytochemically profile 13 wood extracts of the Pinaceae family species, endemic or introduced in Polish flora, using the LC-DAD-ESI-MS/MS method and compare their respective metabolite profiles. Branch wood methanolic extracts were phytochemically profiled. Lignans, stilbenes, flavonoids, diterpenes, procyanidins, and other compounds were detected, with a considerable variety of chemical content among distinct species. Norway spruce (Picea abies (L.) H.Karst.) branch wood was the most abundant source of stilbenes, European larch (Larix decidua Mill.) mostly contained flavonoids, while silver fir (Abies alba Mill.) was rich in lignans. Furthermore, 10 lignans were isolated from the studied material. Our findings confirm that wood industry waste materials, such as conifer branches, can be a potent source of different phytochemicals, with the plant matrix being relatively simple, facilitating future isolation of target compounds.
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