Justin J. J. van der Hooft

Polyphenol Identification Based on Systematic and Robust High-Resolution Accurate Mass Spectrometry Fragmentation

High-mass resolution multi-stage mass spectrometry (MS(n)) fragmentation was tested for differentiation and identification of metabolites, using a series of 121 polyphenolic molecules. The MS(n) fragmentation approach is based on the systematic breakdown of compounds, forming a so-called spectral tree. A chip-based nanoelectrospray ionization source was used combined with an ion-trap, providing reproducible fragmentation, and accurate mass read-out in an Orbitrap Fourier transform (FT) MS enabling rapid assignment of elemental formulas to the molecular ions and all fragment ions derived thereof. The used protocol resulted in reproducible MS(n) fragmentation trees up to MS(5). Obtained results were stable over a 5 month time period, a concentration change of 100-fold, and small changes in normalized collision energy, which is key to metabolite annotation and helpful in structure and substructure elucidation. Differences in the hydroxylation and methoxylation patterns of polyphenolic core structures were found to be reflected by the differential fragmentation of the entire molecule, while variation in a glycosylation site displayed reproducible differences in the relative intensities of fragments originating from the same aglycone fragment ion. Accurate MS(n)-based spectral tree data are therefore a powerful tool to distinguish metabolites with similar elemental formula, thereby assisting compound identification in complex biological samples such as crude plant extracts.

Substructure‐based annotation of high‐resolution multistage MSn spectral trees

RATIONALE High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation. METHODS We present an extended substructure-based approach which allows annotation of hierarchical spectral trees obtained from high-resolution multistage MS(n) experiments. The algorithm yields a hierarchical tree of substructures of a candidate molecule to explain the fragment peaks observed at consecutive levels of the multistage MS(n) spectral tree. A matching score is calculated that indicates how well the candidate structure can explain the observed hierarchical fragmentation pattern. RESULTS The method is applied to MS(n) spectral trees of a set of compounds representing important chemical classes in metabolomics. Based on the calculated score, the correct molecules were successfully prioritized among extensive sets of candidates structures retrieved from the PubChem database. CONCLUSIONS The results indicate that the inclusion of subsequent levels of fragmentation in the automatic annotation of MS(n) data improves the identification of the correct compounds. We show that, especially in the case of lower mass accuracy, this improvement is not only due to the inclusion of additional fragment ions in the analysis, but also to the specific hierarchical information present in the MS(n) spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MS(n) data.

Automatic Chemical Structure Annotation of an LC-MSn Based Metabolic Profile from Green Tea

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.org/magma ) to automatically process and annotate the LC-MS(n) data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MS(n) spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MS(n) (n ≤ 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC-MS(n) run, a total of 116,240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC-MS(n) component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time‐consuming steps in high‐throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely‐available, and open‐source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR‐based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table.

Spectral trees as a robust annotation tool in LC–MS based metabolomics

The identification of large series of metabolites detectable by mass spectrometry (MS) in crude extracts is a challenging task. In order to test and apply the so-called multistage mass spectrometry (MSn) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)–MSn, using crude extracts from both tomato fruit and Arabidopsis leaf. Secondly, the extracts were automatically fractionated by a NanoMate LC-fraction collector/injection robot (Advion) and selected LC-fractions were subsequently analyzed using nanospray-direct infusion to generate offline in-depth MSn spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MSn spectral trees, thereby focusing on two major plant secondary metabolite classes: phenolics and glucosinolates. Following this approach, we were able to discriminate all selected flavonoid glycosides, based on their unique MSn fragmentation patterns in either negative or positive ionization mode. As a proof of principle, we report here 127 annotated metabolites in the tomato and Arabidopsis extracts, including 21 novel metabolites. Our results indicate that online LC–MSn fragmentation in combination with databases of in-depth spectral trees generated offline can provide a fast and reliable characterization and annotation of metabolites present in complex crude extracts such as those from plants.

Structural annotation and elucidation of conjugated phenolic compounds in black, green, and white tea extracts.

Advanced analytical approaches consisting of both LC-LTQ-Orbitrap Fourier transformed (FT)-MS and LC-time-of-flight-(TOF)-MS coupled to solid-phase extraction (SPE) NMR were used to obtain more insight into the complex phenolic composition of tea. On the basis of the combined structural information from (i) accurate mass fragmentation spectra, derived by using LC-Orbitrap FTMS(n), and (ii) proton NMR spectra, derived after LC-TOFMS triggered SPE trapping of selected compounds, 177 phenolic compounds were annotated. Most of these phenolics were glycosylated and acetylated derivatives of flavan-3-ols and flavonols. Principal component analysis based on the relative abundance of the annotated phenolic compounds in 17 commercially available black, green, and white tea products separated the black teas from the green and white teas, with epicatechin-3,5-di-O-gallate and prodelphinidin-O-gallate being among the main discriminators. The results indicate that the combined use of LC-LTQ-Orbitrap FTMS and LC-TOFMS-SPE-NMR leads to a more comprehensive metabolite description and comparison of tea and other plant samples.

NON-SMOKY GLYCOSYLTRANSFERASE1 Prevents the Release of Smoky Aroma from Tomato Fruit

The activity of NON-SMOKY GLYCOSYLTRANSFERASE1 (NSGT1) mediates the conversion of hydrolysis-susceptible glycosides of phenylpropanoid volatiles into hydrolysis-resistant forms and prevents damage-induced release of these volatiles in tomato fruit. This leads to a perceivable reduction in the smoky aroma intensity of these fruits compared to fruits of cultivars containing a truncated NSGT1 gene. Phenylpropanoid volatiles are responsible for the key tomato fruit (Solanum lycopersicum) aroma attribute termed “smoky.” Release of these volatiles from their glycosylated precursors, rather than their biosynthesis, is the major determinant of smoky aroma in cultivated tomato. Using a combinatorial omics approach, we identified the NON-SMOKY GLYCOSYLTRANSFERASE1 (NSGT1) gene. Expression of NSGT1 is induced during fruit ripening, and the encoded enzyme converts the cleavable diglycosides of the smoky-related phenylpropanoid volatiles into noncleavable triglycosides, thereby preventing their deglycosylation and release from tomato fruit upon tissue disruption. In an nsgt1/nsgt1 background, further glycosylation of phenylpropanoid volatile diglycosides does not occur, thereby enabling their cleavage and the release of corresponding volatiles. Using reverse genetics approaches, the NSGT1-mediated glycosylation was shown to be the molecular mechanism underlying the major quantitative trait locus for smoky aroma. Sensory trials with transgenic fruits, in which the inactive nsgt1 was complemented with the functional NSGT1, showed a significant and perceivable reduction in smoky aroma. NSGT1 may be used in a precision breeding strategy toward development of tomato fruits with distinct flavor phenotypes.

A strategy for fast structural elucidation of metabolites in small volume plant extracts using automated MS-guided LC-MS-SPE-NMR.

In many metabolomics studies, metabolite identification by mass spectrometry (MS) often is hampered by the lack of good reference compounds, and hence, NMR information is essential for structural elucidation, especially for the very large group of secondary metabolites. The classical approach for compound identification is to perform time‐consuming and laborious HPLC fractionations and purifications, before (re)dissolving the molecules in deuterated solvents for NMR measurements. Hence, a more direct and easy purification protocol would save time and efforts. Here, we propose an automated MS‐guided HPLC‐MS‐solid phase extraction‐NMR approach, which was used to fully characterize flavonoid structures present in crude tomato plant extracts. NMR spectra of plant metabolites, automatically trapped and purified from LC‐MS traces, were successfully obtained, leading to the structural elucidation of the metabolites. The MS‐based trapping enabled a direct link between the mass signals and NMR peaks derived from the selected LC‐MS peaks, thereby decreasing the time needed for elucidation of the metabolite structures. In addition, automated 1H NMR spectrum fitting further speeded up the candidate rejection process. Our approach facilitates the more rapid unraveling of yet unknown metabolite structures and can therefore make untargeted metabolomics approaches more powerful. Copyright

Structural elucidation of low abundant metabolites in complex sample matrices

Identification of metabolites is a major challenge in biological studies and relies in principle on mass spectrometry (MS) and nuclear magnetic resonance (NMR) methods. The increased sensitivity and stability of both NMR and MS systems have made dereplication of complex biological samples feasible. Metabolic databases can be of help in the identification process. Nonetheless, there is still a lack of adequate spectral databases that contain high quality spectra, but new developments in this area will assist in the (semi-)automated identification process in the near future. Here, we discuss new developments for the structural elucidation of low abundant metabolites present in complex sample matrices. We describe how a recently developed combination of high resolution MS multistage fragmentation (MSn) and high resolution one dimensional (1D)-proton (1H)-NMR of liquid chromatography coupled to solid phase extraction (LC–SPE) purified metabolites can circumvent the need for isolating extensive amounts of the compounds of interest to elucidate their structures. The LC–MS–SPE–NMR hardware configuration in conjunction with high quality databases facilitates complete structural elucidation of metabolites even at sub-microgram levels of compound in crude extracts. However, progress is still required to optimally exploit the power of an integrated MS and NMR approach. Especially, there is a need to improve and expand both MSn and NMR spectral databases. Adequate and user-friendly software is required to assist in candidate selection based on the comparison of acquired MS and NMR spectral information with reference data. It is foreseen that these focal points will contribute to a better transfer and exploitation of structural information gained from diverse analytical platforms.

Rapid and Sustained Systemic Circulation of Conjugated Gut Microbial Catabolites after Single-Dose Black Tea Extract Consumption

Gut microbial catabolites of black tea polyphenols (BTPs) have been proposed to exert beneficial cardiovascular bioactivity. This hypothesis is difficult to verify because the conjugation patterns and pharmacokinetics of these catabolites are largely unknown. The objective of our study was to identify, quantify, and assess the pharmacokinetics of conjugated BTP metabolites in plasma of healthy humans by means of an a priori untargeted LC-MS-based metabolomics approach. In a randomized, open, placebo-controlled, crossover study, 12 healthy men consumed a single bolus of black tea extract (BTE) or a placebo. The relative and, in several cases, absolute concentrations of a wide range of metabolites were determined using U(H)PLC-LTQ-Orbitrap-FTMS. Following BTE consumption, a kinetic response in plasma was observed for 59 BTP metabolites, 11 of these in a quantitative manner. Conjugated and unconjugated catechins appeared in plasma without delay, at 2-4 h, followed by a range of microbial catabolites. Interindividual variation in response was greater for gut microbial catabolites than for directly absorbed BTPs. The rapid and sustained circulation of conjugated catabolites suggests that these compounds may be particularly relevant to proposed health benefits of BTE. Their presence and effects may depend on individual variation in catabolic capacity of the gut microbiota.