Identification Of Unknown Metabolites Research Articles

UGT76F1 glycosylates an isomer of the C7-necic acid component of pyrrolizidine alkaloids in Arabidopsis thaliana.

Identification of unknown metabolites and their biosynthetic genes is an active research area in plant specialized metabolism. By following a gene-metabolite association from a genome-wide association study of Arabidopsis stem metabolites, we report a previously unknown metabolite, 2-hydroxy-2-(1-hydroxyethyl)pentanoic acid glucoside, and demonstrated that UGT76F1 is responsible for its production in Arabidopsis. The chemical structure of the glucoside was determined by a series of analyses, including tandem MS, acid and base hydrolysis, and NMR spectrometry. T-DNA knockout mutants of UGT76F1 are devoid of the glucoside but accumulate increased levels of the aglycone. 2-hydroxy-2-(1-hydroxyethyl)pentanoic acid is structurally related to the C7-necic acid component of lycopsamine-type pyrrolizidine alkaloids such as trachelantic acid and viridifloric acid. Feeding norvaline greatly enhances the accumulation of 2-hydroxy-2-(1-hydroxyethyl)pentanoic acid glucoside in wild-type but not the UGT76F1 knockout mutant plants, providing evidence for an orthologous C7-necic acid biosynthetic pathway in Arabidopsis despite the apparent lack of pyrrolizidine alkaloids.

367 Designing an Untargeted Metabolomics Assay to Detect Biomarkers for Inborn Errors of Metabolism in the Clinical Laboratory

OBJECTIVES/GOALS: To develop an untargeted metabolomics assay that can holistically characterize the small molecule signatures of different inborn errors of metabolism (IEM) for biomarker discovery and identification of novel IEMs, with the goal of implementing the assay into the clinical laboratory to improve testing efficiency. METHODS/STUDY POPULATION: A hydrophilic interaction liquid chromatography (HILIC) column and reverse phase (RP) column were assembled in tandem on a SCIEX X500B quadrupole time-of-flight (QTOF) system to create a dual liquid chromatography (LC), tandem mass spectrometry method. The X500B was operated in data-independent acquisition mode with both positive and negative ionization. A mixture of 165 reference standards from eleven compound classes common to IEMs were used to evaluate the capability of the assay to resolve small molecules. Chromatographic resolution for each standard was determined qualitatively by comparison to a reference spectral database. External validation of the assay will be performed by analyzing a commercial library of reference metabolites. RESULTS/ANTICIPATED RESULTS: A total of 88% (146/165) of the standards were detected by the assay. The RP column alone resolved 71% (117/165) of the standards, the HILIC column resolved 33% (55/165), while 17% (29/165) of the standards were resolved by both columns. The HILIC column resolved standards that were more polar, while the RP column resolved more non-polar compounds. To evaluate matrix effects, the reference standard mixture was spiked into pooled plasma. In the presence of plasma 6/146 (4%) of the standards were suppressed to levels below the limit of detection. We expect external validation with the commercial metabolite library will corroborate these results, and that the high-quality spectral data attained from this reference library can be used to improve identification of unknown metabolites in patient samples. DISCUSSION/SIGNIFICANCE: We have shown our untargeted metabolomics assay can detect known biomarkers for IEMs. Clinical implementation of this method could streamline diagnosis of IEMs while simultaneously improving patient outcomes by leveraging the metabolome for biomarker discovery, and improved understanding of IEM mechanisms to inform novel treatment strategies.

Comprehensive Dissipation of Azadirachtin in Grapes and Tomatoes: The Effect of Bacillus thuringiensis and Tentative Identification of Unknown Metabolites.

Neem oil is a biopesticide normally applied together with Bacillus thuringiensis (Bt). However, neither its dissipation nor the influence of Bt has been previously evaluated. In this study, dissipation of neem oil was investigated when it was applied alone or together with Bt at 3 and 22 °C. A methodology involving solid-liquid extraction and liquid chromatography-high-resolution mass spectrometry was developed for that purpose. The method was validated obtaining recoveries from 87 to 103%, with relative standard deviations lower than 19% and limits of quantification from 5 to 10 μg/kg. Azadirachtin A (AzA) dissipation was fit to a single first order, being faster when neem oil was applied together with Bt and at 22 °C (RL50 = 12-21 days) than alone and at 3 °C (RL50 = 14-25 days). Eight related compounds were found in real samples with similar dissipation curves compared to AzA, and five unknown metabolites were identified in degraded samples, with increasing concentrations during parent compound degradation.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in "pseudo-MS/MS spectra" that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification.

Isomer and Conformer-Specific Mass Distribution-Based Isotopic Shifts in High-Resolution Cyclic Ion Mobility Separations.

Herein, we present the use of mass distribution-based isotopic shifts in high-resolution cyclic ion mobility spectrometry-mass spectrometry (cIMS-MS)-based separations to characterize various isomeric species as well as conformers. Specifically, by using the observed relative arrival time values for the isotopologues found in the isotopic envelope after long pathlength cIMS-MS separations, we were able to distinguish dibromoaniline, dichloroaniline, and quaternary ammonium salt isomers, as well as a pair of 25-hydroxyvitamin D3 conformers based on their respective mass distribution-based shifts. Our observed shifts were highly reproducible and broadly applied to the isotopologues of various atoms (i.e., Cl, Br, and C). Additionally, through a control experiment, we determined that such shifts are indeed pathlength-independent, thus demonstrating that our presented methodology could be readily extended to other high-resolution IMS-MS platforms. These results are the first characterization of conformers using mass distribution-based IMS-MS shifts, as well as the first use of a commercial cIMS-MS platform to characterize isomers via their mass distribution-based shifts. We anticipate that our methodology will have broad applicability for biological analytes and that mass distribution-based shifts could potentially act as an added dimension of analysis in existing IMS-MS workflows in omics-based research. Specifically, we envision that the development of a database of these mass distribution-based shifts could, for example, enable the identification of unknown metabolites in complex matrices.

A rapid and robust method for amino acid quantification using a simple N-hydroxysuccinimide ester derivatization and liquid chromatography-ion mobility-mass spectrometry.

The vast majority of mass spectrometry (MS)-based metabolomics studies employ reversed-phase liquid chromatography (RPLC) to separate analytes prior to MS detection. Highly polar metabolites, such as amino acids (AAs), are poorly retained by RPLC, making quantitation of these key species challenging across the broad concentration ranges typically observed in biological specimens, such as cell extracts. To improve the detection and quantitation of AAs in microglial cell extracts, the implementation of a 4-dimethylaminobenzoylamido acetic acid N-hydroxysuccinimide ester (DBAA-NHS) derivatization agent was explored for its ability to improve both analyte retention and detection limits in RPLC-MS. In addition to the introduction of the DBAA-NHS labeling reagent, a uniformly (U)13C-labeled yeast extract was also introduced during the sample preparation workflow as an internal standard (IS) to eliminate artifacts and to enable targeted quantitation of AAs, as well as untargeted amine submetabolome profiling. To improve method sensitivity and selectivity, multiplexed drift-tube ion mobility (IM) was integrated into the LC-MS workflow, facilitating the separation of isomeric metabolites, and improving the structural identification of unknown metabolites. Implementation of the U-13C-labeled yeast extract during the multiplexed LC-IM-MS analysis enabled the quantitation of 19 of the 20 common AAs, supporting a linear dynamic range spanning up to three orders of magnitude in concentration for microglial cell extracts, in addition to reducing the required cell count for reliable quantitation from 10 to 5 million cells per sample.

Characterization of Metabolites of α-mangostin in Bio-samples from SD Rats by UHPLC-Q-exactive Orbitrap MS.

α-mangostin, a typical xanthone, often exists in Garcinia mangostana L. (Clusiaceae). α-mangostin was found to have a wide range of pharmacological properties. However, its specific metabolic route in vivo remains unclear, while these metabolites may accumulate to exert pharmacological effects, too. This study aimed to clarify the metabolic pathways of α-mangostin after oral administration to the rats. Here, an UHPLC-Q-Exactive Orbitrap MS was used for the detection of potential metabolites formed in vivo. A new strategy for the identification of unknown metabolites based on typical fragmentation routes was implemented. A total of 42 metabolites were detected, and their structures were tentatively identified in this study. The results showed that major in vivo metabolic pathways of α-mangostin in rats included methylation, demethylation, methoxylation, hydrogenation, dehydrogenation, hydroxylation, dehydroxylation, glucuronidation, and sulfation. This study is significant to expand our knowledge of the in vivo metabolism of α-mangostin and to understand the mechanism of action of α-mangostin in rats in vivo.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Enzyme‐Based Chemical Identification for Metabolomics

Background The diversity of chemicals detectable in human samples by high-resolution mass spectrometry (HRMS) exceeds the number of readily available chemical standards, limiting our ability to identify minor metabolites of drugs and other xenobiotics in vivo. Standard workflows for identification of unknown metabolites typically begin by interpretation of ion dissociation spectra (MS/MS) and comparison with library spectra and/or authentic standards – many of which are not available and do not incorporate enzymatic precursor-product relationships as identification criteria. Objective To develop a high-throughput strategy to generate human biotransformation products from diverse xenobiotic and drugs for analysis with LC-HRMS. Hypothesis We hypothesize that unidentified metabolites are generated from enzymatic reactions of known metabolites. Therefore, strategies to generate and characterize metabolic products for diverse arrays of xenobiotics will facilitate identification of metabolites in vivo. Methods Here, we adopt a strategy to generate human biotransformation products of diverse xenobiotics in a high-throughput 96-well plate format using incubations with human liver S9 fractions. Extracts from these reactions were collected at 0 and 24 hour time points and analyzed using LC-HRMS (Thermo Scientific Fusion/High-Field Q-Exactive) to characterize metabolic products generated in a time-dependent manner. Data-dependent MS/MS was performed to collect MS/MS spectra. Expected biotransformation products were characterized by retention time, accurate mass m/z (MS1), MS/MS, and an increase in signal intensity for predicted or previously unreported biotransformation products. Incubations with stable-isotope precursors aided the identification of previously unreported biotransformation products. Results Our data show that known and previously unidentified Phase 1 and Phase 2 metabolites were produced from a range of xenobiotics in a 96-well format using human liver S9 fractions. S9 reaction extracts of 138 drug or xenobiotic precursors were analyzed generating a total of 502 validated metabolites. Selected metabolites were used to support their identification in mouse and human samples based on matched accurate mass MS1, retention time, and co-occurrence with the parent xenobiotic and/or related metabolites in the samples. Conclusion We have developed a scalable tool to generate metabolites from thousands of xenobiotics to facilitate identification of drug and other xenobiotic metabolites in human samples.

Evaluation of Fumaric Acid and Maleic Acid as Internal Standards for NMR Analysis of Protein Precipitated Plasma, Serum, and Whole Blood.

Significant advances have been made in unknown metabolite identification and expansion of the number of quantifiable metabolites in human plasma, serum, and whole blood using NMR spectroscopy. However, reliable quantitation of metabolites is still a challenge. A major bottleneck is the lack of a suitable internal standard that does not interact with the complex blood sample matrix and also does not overlap with metabolite peaks apart from exhibiting other favorable characteristics. With the goal of addressing this challenge, a comprehensive investigation of fumaric and maleic acids as potential internal standards was made along with a comparison with the conventional standards, TSP (trimethylsilylpropionic acid) and DSS (trimethylsilylpropanesulfonic acid). Both fumaric acid and maleic acid exhibited a surprisingly high performance with a quantitation error <1%, while the TSP and DSS caused an average error of up to 35% in plasma, serum, and whole blood. Further, the results indicate that while fumaric acid is a robust standard for all three biospecimens, maleic acid is suitable for only plasma and serum. Maleic acid is not suited for the analysis of whole blood due to its overlap with coenzyme peaks. These findings provide new opportunities for improved and accurate quantitation of metabolites in human plasma, serum, and whole blood using NMR spectroscopy. Moreover, the use of protein precipitation prior to NMR analysis mirrors the sample preparation commonly used for mass spectrometry based metabolomics, such that these findings further strengthen efforts to combine and compare NMR and MS based metabolite data of human plasma, serum, and whole blood for metabolomics based research.

Advances in the development of detection techniques for organophosphate ester flame retardants in food

Organophosphate ester (OPE)-based flame retardants and plasticizers are widely utilized in various industrial products, and are being increasingly used as substitutes for gradually phased brominated flame retardants (BFRs). According to the different types of substituents used, OPEs are mainly divided into alkyls, halogenated compounds, and aromatics, which have widely varying physicochemical properties. OPEs can induce neurotoxicity, carcinogenicity, and damage in endocrine and reproductive systems in humans. Examples of halogenated OPE are tris(2-chloroisopropyl) phosphate (TCIPP) and tris(1,3-dichloropropyl) phosphate (TDCIPP), which are suspected to be carcinogenic. OPEs have emerged as pollutants in environmental and food matrices as a result of volatilization and abrasion processes. Due to its low content in the food matrix and serious background interference, there is a lack of reliable and sensitive analytical methods. Recently, there has been a focus on the detection of OPE flame retardants in food. In this paper, we have reviewed the current status and development trends of OPE detection methods in various foodstuffs. First, the physicochemical properties of more than 30 common OPEs were summarized. Even when using the same extraction solvent, there are obvious differences in extraction efficiency according to different compound properties. To simultaneously analyze multi-component OPE flame retardants in food, it is very important to choose the appropriate extraction solvent to meet the required extraction efficiency of compounds with a wide range of polarities. In addition, although OPE flame retardants are not easily hydrolyzed under neutral conditions, they will degrade to a certain extent under strong acidic and alkaline conditions. It is worth mentioning that avoiding the removal of lipids and other interferences in food matrices under strong acidic and alkaline conditions. Different pretreatment methods, such as accelerated solvent extraction, matrix solid-phase dispersion extraction, microwave-assisted extraction, ultrasonic-assisted extraction, QuEChERS, solid-phase extraction, gel permeation chromatography, and dispersive solid-phase extraction are also compared. Combining the advantages of ultrasonic assisted extraction (UAE) and QuEChERS pretreatment technology can reduce the waste of extraction solvent and internal standard solution. For lipid-rich matrices like biological samples, it is necessary to remove lipid interference by SPE columns or GPC purification. Furthermore, the characteristics of separation and detection techniques, such as GC, GC-MS/MS, and LC-MS/MS, are discussed. Comparing detection limits and recovery data with those reported in the literature, GC-MS/MS can provide improved selectivity, precision, and limits of detection in complex food matrices, but LC-MS often suffers from ion suppression, matrix interferences, and incomplete separation of some OPEs. Since electron impact (EI) has higher ionization efficiency, it produces many fragment ions, thus creating a more complete spectral library, which is conducive to structural identification. When using GC-MS/MS to determine OPE flame retardants, the EI mode was usually used. However, positive chemical ionization (PCI) and electron capture negative ionization (ECNI) modes were also used sometimes. In the section on quality control, the main sources of standards and internal standards, possible sources of blank contamination, and the research status of measures to reduce matrix effects have been reviewed. To avoid blank contamination, all the laboratory equipment should be carefully cleaned, heated at high temperatures, and rinsed with polar or non-polar organic solvents in order to remove all interfering organic residues. Isotopically labeled internal standard and isotopic dilution mass spectrum quantification methods are used to reduce matrix effects. Owing to the limited availability of commercial standards and the relatively high cost, alternative approaches, such as matrix-matched calibration or standard addition methods, are required. The screening and identification of unknown metabolites of OPEs and related analytical methods based on high resolution mass spectrometry could also be studied for precursor OPEs in foodstuffs in the future.

Untargeted Metabolomics of Arabidopsis Stomatal Immunity.

Although untargeted metabolomic approaches hold great promise for global identification of low molecular weight metabolites in biological samples, deep coverage and confident identification of the metabolites remains challenging due to the great diversity and number of chemical structures, especially in plants. Additionally, there is a need to employ a cell-specific research approach to many physiological and biological responses to specific environmental stimuli. Here, we report an untargeted metabolomic method using Arabidopsis thaliana guard cell samples during response to systemic signals of pathogen attack. We employed a new Acquire X MSn data acquisition technology, which uses an iterative fragmentation process to increase level-2 identification of unknown metabolites. We were able to increase the number of identified metabolites and thus the metabolome coverage in Arabidopsis guard cells. This method can be applied to studying metabolomes of other cell types and tissues.

Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (tR) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R2) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure–ion intensity relationship (QSIIR) model.

Mass spectral fragmentation of perfluoroacyl derivatives of half nitrogen mustards for their detection by gas chromatography/mass spectrometry.

Analytical methods for the detection and identification of half nitrogen mustards (halfNMs), i.e., partially hydrolyzed products of nitrogen mustards (pHpNMs), using silyl derivatives are often associated with low sensitivity and selectivity. In order to overcome these limitations, the derivatization of halfNMs was performed using perfluoroacylation. Two efficient derivatization techniques using trifluoroacetyl (TFA) and heptafluorobutyryl (HFB) groups were developed for the unambiguous identification of halfNMs. A mass spectral database was generated by performing gas chromatography/electron ionization mass spectrometry (GC/EI-MS) and gas chromatography/positive chemical ionization mass spectrometry (GC/PCI-MS). The fragmentation pathways were studied by tandem mass spectrometry (MS/MS) in both EI and PCI mode. The EI-MS spectra of the TFA and HFB derivatives of halfNMs contain intense molecular ions and fragment ions, thus making perfluoroacylation preferable to silylation. In addition, the background-free chromatogram obtained using these derivatives provides unambiguous identification of these compounds in blind samples. The structures of the fragment ions were postulated, and the sources of significant ions were traced by performing MS/MS precursor ion scans. In the PCI-MS spectra, along with the protonated molecule, significant peaks due to neutral losses of HF, HCl, CH3 Cl and CF3 COOH were observed. This is the first report of the elucidation of the fragmentation pathways of perfluoroacyl derivatives of halfNMs. The complementary GC/PCI-MS and GC/PCI-MS/MS data will be helpful in the identification of unknown metabolites in a fast and reliable fashion.

Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics.

Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap FusionTM TribridTM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma.

A feasible strategy based on isotopic fine structures to enhance the reliability of metabolite identification by Fourier transform ion cyclotron resonance mass spectrometry.

In the process of the identification of unknown metabolites, the most important thing is to determine their real chemical formulae according to the accurate masses which are determined by high-resolution mass spectrometry. However, high mass accuracy alone is not enough to exclude false candidates. Use of isotopic fine structures (IFSs) derived from Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) as a single further constraint could decisively determine the molecular formulae for unknown metabolites. Gastrodin, an active constituent from Gastrodia elata Bl., which can penetrate through the blood-brain barrier and rapidly decompose to p-hydroxybenzyl alcohol in the brain, was selected as a model drug. The accurate masses, possible chemical formulae and IFSs of its metabolites in rat plasma were acquired using FT-ICR MS. Besides gastrodin, a total of eight metabolites including two phase I and six phase II metabolites were detected. Their chemical formulae were decisively determined by IFSs. Furthermore, their chemical structures were identified by comparing their fragment ions with those of gastrodin. Results indicated the metabolic pathways of gastrodin in rats including deglycosylation, oxidation, glucuronidation, sulfate conjugation and glycine conjugation. It is demonstrated that IFSs are effective in unambiguous determination of chemical formulae of metabolites. It could be used as a feasible strategy to enhance the reliability of metabolite identification in drug metabolism studies.

Accurate and Efficient Determination of Unknown Metabolites in Metabolomics by NMR-Based Molecular Motif Identification.

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.

Extractive Ratio Analysis NMR Spectroscopy for Metabolite Identification in Complex Biological Mixtures

The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.

Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification.

Tandem mass spectrometry (MS/MS) is the workhorse for structural annotation of metabolites, because it can provide abundance of structural information. Currently, metabolite identification mainly relies on querying experimental spectra against public or in-house spectral databases. The identification is severely limited by the available spectra in the databases. Although, the metabolome consists of a huge number of different functional metabolites, the whole metabolome derives from a limited number of initial metabolites via bioreactions. In each bioreaction, the reactant and the product often change some substructures but are still structurally related. These structurally related metabolites often have related MS/MS spectra, which provide the possibility to identify unknown metabolites through known ones. However, it is challenging to explore the internal relationship between MS/MS spectra and structural similarity. In this study, we present the deep-learning-based approach for MS/MS-aided structural-similarity scoring (DeepMASS), which can score the structural similarity of unknown metabolite against the known one with MS/MS spectra and deep neural networks. We evaluated DeepMASS with leave-one-out cross-validation on MS/MS spectra of 662 compounds in KEGG and an external test on the biomarkers from male infertility study measured on Shimadzu LC-ESI-IT-TOF and Bruker Compact LC-ESI-QTOF. Results show that the identification of unknown compound is valid if its structure-related metabolite is available in the database. It provides an effective approach to extend the identification range of metabolites for existing MS/MS databases.