Metabolomic database annotations query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm-0

<p><b>Copyright information:</b></p><p>Taken from "Metabolomic database annotations query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm"</p><p>BMC Bioinformatics 2006;7():234-234.</p><p>Published online 28 Apr 2006</p><p>PMCID:PMC1464138.</p><p>Copyright © 2006 Kind and Fiehn; licensee BioMed Central Ltd.</p

Metabolomic database annotations query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm-3

<p><b>Copyright information:</b></p><p>Taken from "Metabolomic database annotations query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm"</p><p>BMC Bioinformatics 2006;7():234-234.</p><p>Published online 28 Apr 2006</p><p>PMCID:PMC1464138.</p><p>Copyright © 2006 Kind and Fiehn; licensee BioMed Central Ltd.</p> formulae

Using Accurate Mass Gas Chromatography–Mass Spectrometry with the MINE Database for Epimetabolite Annotation

Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography–accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the “metabolic in-silico expansion” database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying 10 methylated nucleosides and 6 methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems

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

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, <i>m</i>/<i>z</i> 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

How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra

Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) is a machine learning tool to predict in silico tandem mass spectra (MS/MS) for known or suspected metabolites for which chemical reference standards are not available. As a machine learning tool, it relies on both an underlying statistical model and an explicit training set that encompasses experimental mass spectra for specific compounds. Such mass spectra depend on specific parameters such as collision energies, instrument types, and adducts which are accumulated in libraries. Yet, ultimately prediction tools that are meant to cover wide expanses of entities must be validated on cases that were not included in the initial training and testing sets. Hence, we here benchmarked the performance of CFM-ID 4.0 to correctly predict MS/MS spectra for spectra that were not included in the CFM-ID training set and for different mass spectrometry conditions. We used 609,456 experimental tandem spectra from the NIST20 mass spectral library that were newly added to the previous NIST17 library version. We found that CFM-ID’s highest energy prediction output would maximize the capacity for library generation. Matching the experimental collision energy with CFM-ID’s prediction energy produced the best results, even for HCD-Orbitrap instruments. For benzenoids, better MS/MS predictions were achieved than for heterocyclic compounds. However, when exploring CFM-ID’s performance on 8,305 compounds at 40 eV HCD-Orbitrap collision energy, >90% of the 20/80 split test compounds showed <700 MS/MS similarity score. Instead of a stand-alone tool, CFM-ID 4.0 might be useful to boost candidate structures in the greater context of identification workflows

Ultrafast Polyphenol Metabolomics of Red Wines Using MicroLC-MS/MS

The taste and quality of red wine are determined by its highly complex mixture of polyphenols and many other metabolites. No single method can fully cover the full metabolome, but even for polyphenols and related compounds, current methods proved inadequate. We optimized liquid chromatography resolution and sensitivity using 1 mm i.d. columns with microLC pumps and compared data-dependent to data-independent (SWATH) MS/MS acquisitions. A high-throughput microLC-MS method was developed with a 4 min gradient at 0.05 mL/min flow rate on a Kinetex C18 column and Sciex TripleTOF mass spectrometry. Using the novel software MS-DIAL, we structurally annotated 264 compounds including 165 polyphenols in six commercial red wines by accurate mass MS/MS matching. As proof of concept, multivariate statistics revealed the difference in the metabolite profiles of the six red wines, and regression analysis linked the polyphenol contents to the taste of the red wines

Weighting Low-Intensity MS/MS Ions and <i>m</i>/<i>z</i> Frequency for Spectral Library Annotation

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental

Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics

Here, we provide an algorithm that introduces excited states into the molecular dynamics prediction of the 70 eV electron ionization mass spectra. To decide the contributions of different electronic states, the ionization cross section associated with relevant molecular orbitals was calculated by the binary–encounter–Bethe (BEB) model. We used a fast orthogonalization model/single and double state configuration interaction (OM2/CISD) method to implement excited states calculations and combined this with the GFN1-xTB semiempirical model. Demonstrated by predicting the mass spectrum of urocanic acid, we showed better accuracies to experimental spectra using excited-state molecular dynamics than calculations that only used the ground-state occupation. For several histidine pathway intermediates, we found that excited-state corrections yielded an average of 73% more true positive ions compared to the OM2 method when matching to experimental spectra and 16% more true positive ions compared to the GFN method. Importantly, the exited state models also correctly predict several fragmentation reactions that were missing from both ground-state methods. Overall, for 48 calculated molecules, we found the best average mass spectral similarity scores for the mixed excited-state method compared to the ground-state methods using either cosine, weighted dot score, or entropy similarity calculations. Therefore, we recommend adding excited-state calculations for predicting the electron ionization mass spectra of small molecules in metabolomics

Environmental Tobacco Smoke Alters Metabolic Systems in Adult Rats

Human exposure to environmental tobacco smoke (ETS) is associated with an increased incidence of pulmonary and cardiovascular disease and possibly lung cancer. Metabolomics can reveal changes in metabolic networks in organisms under different physio-pathological conditions. Our objective was to identify spatial and temporal metabolic alterations with acute and repeated subchronic ETS exposure to understand mechanisms by which ETS exposure may cause adverse physiological and structural changes in the pulmonary and cardiovascular systems. Established and validated metabolomics assays of the lungs, hearts. and blood of young adult male rats following 1, 3, 8, and 21 days of exposure to ETS along with day-matched sham control rats (<i>n</i> = 8) were performed using gas chromatography time-of-flight mass spectrometry, BinBase database processing, multivariate statistical modeling, and MetaMapp biochemical mapping. A total of 489 metabolites were measured in the lung, heart, and blood, of which 142 metabolites were identified using a standardized metabolite annotation pipeline. Acute and repeated subchronic exposure to ETS was associated with significant metabolic changes in the lung related to energy metabolism, defense against reactive oxygen species, substrate uptake and transport, nucleotide metabolism, and substrates for structural components of collagen and membrane lipids. Metabolic changes were least prevalent in heart tissues but abundant in blood under repeated subchronic ETS exposure. Our analyses revealed that ETS causes alterations in metabolic networks, especially those associated with lung structure and function and found as systemic signals in the blood. The metabolic changes suggest that ETS exposure may adversely affects the mitochondrial respiratory chain, lung elasticity, membrane integrity, redox states, cell cycle, and normal metabolic and physiological functions of the lungs, even after subchronic ETS exposure

Environmental Tobacco Smoke Alters Metabolic Systems in Adult Rats

Human exposure to environmental tobacco smoke (ETS) is associated with an increased incidence of pulmonary and cardiovascular disease and possibly lung cancer. Metabolomics can reveal changes in metabolic networks in organisms under different physio-pathological conditions. Our objective was to identify spatial and temporal metabolic alterations with acute and repeated subchronic ETS exposure to understand mechanisms by which ETS exposure may cause adverse physiological and structural changes in the pulmonary and cardiovascular systems. Established and validated metabolomics assays of the lungs, hearts. and blood of young adult male rats following 1, 3, 8, and 21 days of exposure to ETS along with day-matched sham control rats (<i>n</i> = 8) were performed using gas chromatography time-of-flight mass spectrometry, BinBase database processing, multivariate statistical modeling, and MetaMapp biochemical mapping. A total of 489 metabolites were measured in the lung, heart, and blood, of which 142 metabolites were identified using a standardized metabolite annotation pipeline. Acute and repeated subchronic exposure to ETS was associated with significant metabolic changes in the lung related to energy metabolism, defense against reactive oxygen species, substrate uptake and transport, nucleotide metabolism, and substrates for structural components of collagen and membrane lipids. Metabolic changes were least prevalent in heart tissues but abundant in blood under repeated subchronic ETS exposure. Our analyses revealed that ETS causes alterations in metabolic networks, especially those associated with lung structure and function and found as systemic signals in the blood. The metabolic changes suggest that ETS exposure may adversely affects the mitochondrial respiratory chain, lung elasticity, membrane integrity, redox states, cell cycle, and normal metabolic and physiological functions of the lungs, even after subchronic ETS exposure