Systematic metabolite annotation and identification in complex biological extracts : combining robust mass spectrometry fragmentation and nuclear magnetic resonance spectroscopy

Detailed knowledge of the chemical content of organisms, organs, tissues, and cells is needed to fully characterize complex biological systems. The high chemical variety of compounds present in biological systems is illustrated by the presence of a large variety of compounds, ranging from apolar lipids, semi-polar phenolic conjugates, toward polar sugars. A molecules’ chemical structure forms the basis to understand its biological function. The chemical identification process of small molecules (i.e., metabolites) is still one of the major focus points in metabolomics research. Actually, no single analytical platform exists that can measure and identify all existing metabolites. In this thesis, two analytical techniques that are widely used within metabolite identification studies have been combined, i.e. mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). MS was used to ionize the metabolites and to record their molecular weight and to provide substructure information based on fragmentation in the mass spectrometer. NMR gave the comprehensive structural information on the chemical environment of protons and their linkage to other protons within the molecule. The additional structural information as compared to MS is at the cost of an increased amount of compound needed for NMR detection and spectra generation. Here we combined both analytical methods into a liquid chromatography (LC)-based platform that concentrated compounds based on their specific mass; thereby providing a direct link between MS and NMR data. Another platform was developed that generated robust multistage MSn data, i.e., the systematic fragmentation of metabolites and subsequent fragmentation of resulting fragments. This thesis aims to accelerate metabolite identification of low abundant plant and human derived compounds by following a systematic approach. The acquired structural information from MSn and 1D-1H-NMR spectra resulted in the complete elucidation of phenolic metabolites in microgram scale from both plant and human origin. In the chapter 1, the analytical techniques and terms used throughout the thesis are introduced. The second chapterdescribes how a high mass resolution MSn fragmentation approach was tested in both negative and positive ionization modes for differentiation and identification of metabolites, using a series of 121 polyphenolic molecules. An injection robot was used to infuse the reference compounds one by one into a hybrid mass spectrometer, combining MSn possibilities with accurate mass read-out. This approach resulted in reproducible and robust MSn fragmentation trees up to MS5, which were differential even for closely related compounds. Accurate MSn-based spectral trees were shown to be robust and powerful to distinguish metabolites with similar elemental formula (i.e. isomers), thereby assisting compound identification and annotation in complex biological samples. In the third chapter, we tested the annotation power of this spectral tree approach for annotation of phenolic compounds in crude extracts from Lycopersicum esculentum(tomato) and the model plant Arabipopsis thaliana. Partial MSn spectral trees were generated directly after chromatographic elution (LC-MSn). Detailed MSn spectral trees could be recorded with the use of a collector/injector robot.We were able to discriminate flavonoid glycosides based on their unique MSn fragmentation patterns in either negative or positive ionization mode. Following this approach, we could annotate 127 metabolites in the tomato and Arabidopsis extracts, including 21 novel metabolites. The good quality MSn spectral trees obtained can be used to populate MSn databases and the protocols to generate the spectral trees are a good basis to further expand this database with more diverse compounds. Chapter 4 then describes how an automated platform, coupling chromatography with MS and NMR (LC-MS-solid phase extraction-NMR), was developed that can trap and transfer metabolites based on their mass values from a complex biological extract in order to obtain NMR spectra of the trapped LC-MS peak, out of minute amounts of sample and analyte. Extracts from tomatoes modified in their flavonoid biosynthesis pathway were used as proof of principle for the metabolite identification process. This approach resulted in the complete structural elucidation of 10 flavonoid glycosides. This study shows that improving the link between the mass signals and NMR peaks derived from the selected LC-MS peaks decreases the time needed for elucidation of the metabolite structures. In addition, automated 1D-1H-NMR spectrum fitting of the experimental data obtained in this study using the PERCH NMR software further speeded up the candidate rejection process. Chapter 5 illustrates how the two developed analytical platforms could be used for the successful selection, annotation, and identification of 177 phenolic compounds present in different extracts of Camellia sinensis, i.e. green, white, and black tea extracts, including the full identification of microgram amounts of complex acylated conjugates of kaempferol and quercetin. 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, thereby illustrating the differential phenolic metabolite contents of black tea as compared to green and white teas. The change in phenolic profiles reflects the polymerization reactions occurring upon transformation of green tea into black tea. This study shows that the combined use of MSn spectral trees and LC-MS-solid phase extraction-NMR leads to a more comprehensive metabolite description thereby facilitating the comparison of tea and other plant samples. In chapter 6, we aimed to structurally elucidate and quantify polyphenol-derived conjugates present in the human body by studying the urinary excretion of these conjugates.We applied a combination of a solid phase extraction preparation step and the two HPLC-coupled analytical platforms as described in chapters 2 and 3. This analytical strategy resulted in the annotation of 138 urinary metabolites including 35 completely identified valerolactone conjugates. These valerolactones are microbial break-down products of tea phenols. NMR predictions of glucuronidated and sulphonated core metabolites were performed in order to confirm the NMR peak assignments on the basis of 1D-1H-NMR data only. In addition, 26 hours quantitative excretion profiles for certain valerolactone conjugates were obtained using diagnostic proton signals in the 1D-1H-NMR spectra of urine fractions. In the seventh chapter, the current state of metabolite identification and expected challenges in the structural elucidation of metabolites at (sub)microgram amounts are discussed. The work in this thesis and of other groups working on the hyphenation of MS and NMR shows that the complete de novo identification of microgram amounts and even lower of compound is feasible by using MS guided solid phase extractiontrapping in combination with 1D-1H-NMR or UPLC-TOF-MS isolation followed by capillary NMR. Semi-automated annotation of compounds based on their MS and NMR features is now feasible for some well studied compound classes and groups. Altogether, the developed platforms yield new and improved insights in the phenolic profiles of well-studied plants as well as a comprehensive picture of the metabolic fate of green tea polyphenols upon intake in the human body. The followed metabolite identification strategy is useful for other studies that aim to elucidate bioactive compounds, especially when only small sample volumes are available. This thesis also contributes to the acquisition of good quality data for metabolite identification by acquiring robust MSn fragmentation spectra and 1D-1H-NMR spectra of partial purified analytes at microgram scale, which paves the path for further developments in data acquisition and analysis, as well as the unravelling of yet unknown metabolites in a faster, more systematic and automated manner. </p

A community-driven paired data platform to accelerate natural product mining by combining structural information from genomes and metabolomes

Natural products are small molecules produced by bacteria, fungi, and plants that have a large variety of functions, including chemical defence and communication. Moreover, many of those natural products are exploited for therapeutic or medicinal use. For example, many antibiotics have natural products as origin ..

Thermal diffusivity of periderm from tomatoes of different maturity stages as determined by the concept of the frequency-domain open photoacoustic cell

The frequency-domain open photoacoustic cell (OPC) approach was used to determine room temperature thermal diffusivity of skins (pericarps) from the raw tomatoes (Lycopersicon esculetum Mill.) characterized by the three different stages of ripeness (from immature-green to a mature-red). Periodically interrupted 532 nm laser radiation was used to heat the dry tomato skins, typically 10 mm in diameter and up to 68 µm thick; the modulating frequency f varied from 8 to 150 Hz. Initially, a combined OPC-model that takes into account both, the thermoelastic bending and the effect of thermal diffusion (TD), has been applied. Preliminary results showed that until at least 40 Hz, the effect of TD dominates; above this value the combined model fits the experimental data only poorly. For this reason a less complex OPC-TD approach was applied to all investigated skins instead, which predicts an exponential decrease for the amplitude of measured photoacoustic signal S with increasing f. For a specimen that is simultaneously opaque and thermally thick, S depends on f as S~exp(-b f1/2) where b is a fitting parameter. The S versus f plot enables one to deduce the numerical value for b which, on its turn allows for the assessment of skin’s thermal diffusivity a. Thermal diffusivities obtained for the immature green, orange, and red skins (periderms) are 9.9×10-8 m2¿s-1, 7.2×10-8 m2¿s-1, and 4.6×10-8 m2¿s-1, respectively; the uncertainty was typically 5% of the measured value

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Microbial Biotechnolog

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed

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 (MS n ) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)–MS n , 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 MS n spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MS n 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 MS n 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–MS n 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

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 (MS n ) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)–MS n , 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 MS n spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MS n 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 MS n 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–MS n 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 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 platform

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

Fast and reliable metabolite identification based on automated MS-guided HPLC-MS-SPE-NMR metabolite extraction combined with an automated 1H NMR spectrum fitting was developed. Positional isomers as structure 1 and 2 were easily distinguished. 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 powerfu