Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite association network. Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS. Consequently, a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude in rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides an important protocol for exploring phytochemical diversity in other plant species.     

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)?CMS 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?CMS 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.     

Computational annotation of plant metabolomics profiles via a novel network-assisted approach

Mass spectrometry (MS) has become the analytical method of choice in plant metabolomics. Nevertheless, metabolite annotation remains a major challenge and implies the integration of structural searches in compound libraries with biological knowledge inferred from metabolite regulation studies. Here we propose a novel integrative approach to process and exploit the rich structural information contained in in-source fragmentation patterns of high-resolution LC–MS profiles. In this approach, a correlation matrix is first calculated from individual mass features extracted by xcms processing. Mass feature co-regulation patterns corresponding to metabolite in-source fragmentation are then detected and assembled into compound spectra using the R package CAMERA and processed for in silico fragment-based structure elucidation using MetFrag. We validate the performance of this approach for the rapid annotation of the twelve largest compound spectra, including four O-acyl sugars and six 17-hydroxygeranyllinalool diterpene glycosides in metabolic profiles of insect-attacked Nicotiana attenuata leaves. Additionally, we demonstrate the power of refining MetFrag metabolite annotations based on co-regulation patterns between known and unknown compounds in the correlation matrix and proposed structural annotations on two previously un-characterized O-acyl sugars. In summary, this novel approach facilitates compound annotation from in-source fragmentation patterns using correlation between intensities of mass features of one or several metabolites. Additionally, this analysis provides further support that insect herbivory activates major metabolic reconfigurations in N. attenuata leaves.     

RIKEN tandem mass spectral database (ReSpect) for phytochemicals: A plant-specific MS/MS-based data resource and database

The fragment pattern analysis of tandem mass spectrometry (MS/MS) has long been used for the structural characterization of metabolites. The construction of a plant-specific MS/MS data resource and database will enable complex phytochemical structures to be narrowed down to candidate structures. Therefore, a web-based database of MS/MS data pertaining to phytochemicals was developed and named ReSpect (RIKEN tandem mass spectral database). Of the 3595 metabolites in ReSpect, 76% were derived from 163 literature reports, whereas the rest was obtained from authentic standards. As a main web application of ReSpect, a fragment search was established based on only the m/z values of query data and records. The confidence levels of the annotations were managed using the MS/MS fragmentation association rule, which is an algorithm for discovering common fragmentations in MS/MS data. Using this data resource and database, a case study was conducted for the annotation of untargeted MS/MS data that were selected after quantitative trait locus analysis of the accessions (Gifu and Miyakojima) of a model legume Lotus japonicus. In the case study, unknown metabolites were successfully narrowed down to putative structures in the website.     

地衣型真菌石果衣(疣孢菌科，子囊菌门)次级代谢产物的多样性

地衣能够产生大量的新型次级代谢产物,但以往对石果衣Endocarpon pusillum的研究中未能检测出任何次级代谢产物。然而,对其共生菌进行基因组测序发现其中含有14个沉默的PKS基因和2个沉默的NRPS基因。在此研究中,为激活其途径,使用了优化马铃薯培养基和大米培养基对石果衣共生真菌进行了培养。从优化马铃薯培养物中分离得到9个次级代谢产物,包括2个新的异吲哚-1-酮类化合物(1, 2)。而从大米培养物中分离到3个已知化合物和1个新的萘醌类化合物(9)。通过核磁共振和质谱数据确定了新化合物的结构。研究结果表明,大量地衣中未能检测出任何次级代谢产物,或仅能检测出少量次级代谢产物,可能与其基因组中的沉默基因有关。因此,通过对沉默基因的激活方法为地衣次级代谢产物资源的研究与开发开辟了有效途径。     

A multimodal metabolomics approach using imaging mass spectrometry and liquid chromatography-tandem mass spectrometry for spatially characterizing monoterpene indole alkaloids secreted from roots

Plants release specialized (secondary) metabolites from their roots to communicate with other organisms, including soil microorganisms. The spatial behavior of such metabolites around these roots can help us understand roles for the communication; however, currently, they are unclear because soil-based studies are complex. Here, we established a multimodal metabolomics approach using imaging mass spectrometry (IMS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially assign metabolites under laboratory conditions using agar. In a case study using Catharanthus roseus, we showed that 58 nitrogen (N)-containing metabolites are released from the roots into the agar. For the metabolite assignment, we used ¹⁵N-labeled and non-labeled LC-MS/MS data, previously reported. Four metabolite ions were identified using authentic standard compounds as derived from monoterpene indole alkaloids (MIAs) such as ajmalicine, catharanthine, serpentine, and yohimbine. An alkaloid network analysis using dot products and spinglass methods characterized five clusters to which the 58 ions belong. The analysis clustered ions from the indolic skeleton-type MIAs to a cluster, suggesting that other communities may represent distinct metabolite groups. For future chemical assignments of the serpentine community, key fragmentation patterns were characterized using the ¹⁵N-labeled and non-labeled MS/MS spectra.     

Metabolomic profiling from formalin-fixed, paraffin-embedded tumor tissue using targeted LC/MS/MS: application in sarcoma

The relatively new field of onco-metabolomics attempts to identify relationships between various cancer phenotypes and global metabolite content. Previous metabolomics studies utilized either nuclear magnetic resonance spectroscopy or gas chromatography/mass spectrometry, and analyzed metabolites present in urine and serum. However, direct metabolomic assessment of tumor tissues is important for determining altered metabolism in cancers. In this respect, the ability to obtain reliable data from archival specimens is desirable and has not been reported to date. In this feasibility study, we demonstrate the analysis of polar metabolites extracted directly from ten formalin-fixed, paraffin-embedded (FFPE) specimens, including five soft tissue sarcomas and five paired normal samples. Using targeted liquid chromatography-tandem mass spectrometry (LC/MS/MS) via selected reaction monitoring (SRM), we detect an average of 106 metabolites across the samples with excellent reproducibility and correlation between different sections of the same specimen. Unsupervised hierarchical clustering and principal components analysis reliably recovers a priori known tumor and normal tissue phenotypes, and supervised analysis identifies candidate metabolic markers supported by the literature. In addition, we find that diverse biochemical processes are well-represented in the list of detected metabolites. Our study supports the notion that reliable and broadly informative metabolomic data may be acquired from FFPE soft tissue sarcoma specimens, a finding that is likely to be extended to other malignancies.     

Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS

An overview is presented of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS), the two major hyphenated techniques employed in metabolic profiling that complement direct 'fingerprinting' methods such as atmospheric pressure ionization (API) quadrupole time-of-flight MS, API Fourier transform MS, and NMR. In GC/MS, the analytes are normally derivatized prior to analysis in order to reduce their polarity and facilitate chromatographic separation. The electron ionization mass spectra obtained are reproducible and suitable for library matching, mass spectral collections being readily available. In LC/MS, derivatization and library matching are at an early stage of development and mini-reviews are provided. Chemical derivatization can dramatically increase the sensitivity and specificity of LC/MS methods for less polar compounds and provides additional structural information. The potential of derivatization for metabolic profiling in LC/MS is demonstrated by the enhanced analysis of plant extracts, including the potential to measure volatile acids such as formic acid, difficult to achieve by GC/MS. The important role of mass spectral library creation and usage in these techniques is discussed and illustrated by examples.     

Metabolites from the Dark Septate Endophyte Drechslera sp. Evaluation by LC/MS and Principal Component Analysis of Culture Extracts with Histone Deacetylase Inhibitors

Secondary metabolites from the cultures of the dark septate fungal endophyte (DSE) Drechslera sp., isolated from the roots of rye grass (Lollium sp.) and cultured under different experimental conditions, are described here for the first time. The use of suberoylanilidehydroxamic acid (SAHA) and other histone deacetylase inhibitors as epigenetic modifiers in the culture medium was evaluated by LC/MS and LC/MS/MS. Several differences in the metabolite production were detected by means of supervised principal component analysis (PCA) of LC/MS data. The presence of the compounds in the culture medium or in the mycelium was compared. In order to confirm their structure, many of these natural products were isolated from a larger scale culture. These metabolites were characterized as prenylhydroxybenzoic acids and chromans, two compounds, one of each class were previously undescribed, prenylquinoids, diketopiperazines and macrosphelides. Some of the compounds, which were released to the medium, showed good antifungal activity, suggesting that these compounds could protect Lollium from fungal phytopatogens. The use of SAHA as an additive of the cultures also induced the release of hexosylphytosphyngosine to the culture medium. The biotransformation of the inhibitors was observed in addition to the production of antifungal metabolites, showing the ability of this endophytic strain to control xenobiotics.     

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Background  

Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging

MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals’ germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.     

Metabolite Profiling in Withania somnifera Roots Hydroalcoholic Extract Using LC/MS,GC/MS and NMR Spectroscopy

Ashwagandha (Withania somnifera) is a very well‐known herbal medicine and it was well studied for its active metabolites throughout the World. Although, nearly 40 withanolides were isolated from W. somnifera root extract, still there is remaining unidentified metabolites due to very low abundance and geographical variation. Advanced separation technology with online identification by mass and nuclear magnetic resonance (NMR) are nowadays used to find out the new compounds in the crude herbal extract. This article described the metabolite profiling of ashwagandha root hydroalcoholic extract using ultra‐performance liquid chromatography coupled with a positive ion electrospray ionization tandem mass spectrometry through gas chromatography mass spectrometry (GC/MS) and NMR spectroscopy. A total of 43 possible withanolides was identified and proposed their structures based on the mass of molecular and fragment ions. GC/MS and NMR analysis indicated the presence of several known withanolides including withaferin A, withanolide D, withanoside IV or VI, withanolide sulfoxide, etc. To the best of our knowledge, dihydrowithanolide D at m/z 473 (t_R 7.86 min) and ixocarpalactone A at m/z 505 (t_R 8.43 min) were first time identified in the ashwagandha root hydroalcoholic extract. The current study that described the identification of withanolides with summarized literature review might be helpful for designing the experiment to identify of the new chemical constituents in Withania species.     

Identification of novel lignans in the whole grain rye bran by non-targeted LC–MS metabolite profiling

Rye (Secale cereale) is among the richest dietary sources of lignan phytochemicals. Lignans are one of the suggested metabolite groups to contribute to the beneficial health effects of whole grain products evidenced in epidemiological studies. So far, the complete repertoire of lignan derivatives in rye, especially in the bran, has not been fully described. In this study, ten novel oligomeric sesqui- and dilignans were identified in rye bran by the use of high resolution LC?CMS analysis (i.e., UPLC-qTOF-MS/MS). Putative identification of lignan components in the bran was performed by combining: (i) detailed inspection of the fragmentation behavior of available standard compounds belonging to different lignan types, (ii) interpretation of MS/MS data obtained from unknown metabolites in the samples. This combined analysis, particularly detailed MS/MS characterization, is most valuable for non-targeted assays in metabolite-rich matrices such as plant extracts, in which the verification of identity with authentic standards for each detected metabolite is normally not possible. Metabolomics analysis will increasingly aid in deciphering the active compounds in dietary products as part of studies aiming at elucidating the link between human health and nutrition.     

Application of LC/MS systems to structural characterization of flavonoid glycoconjugates

Mass spectrometry is currently one of the most versatile and sensitive instrumental methods applied to structural characterization of plant secondary metabolite mixtures isolated from biological material. Plant tissues contain thousands of natural products fulfilling different roles in plant physiology and biochemistry. These natural products have various biological activities in respect to plants synthesizing them, in their responses to different environmental stresses and are also active principles of food supplements and pharmaceuticals of plant origin. Flavonoids constitute a large group of phenolic secondary metabolites and are probably produced by all terrestrial plant species. More than 9000 glycoconjugates of flavonoids are presently known in the plant kingdom and more than 50 of them may be present in a single plant. For this reason methods of identification and analysis of this group of compounds are particularly demanded. Due to a high number of metabolites present in plant extracts, the isolation and purification of most compounds in amounts suitable for unambiguous characterization with NMR methods is often impossible. For these reasons elaboration of strategies for sufficiently precise structural characterization of compounds present in mixture samples is currently a primary task. Mass spectrometry, thanks to application of different physical phenomena for ionization, separation and detection of analyzed molecules, became the method of choice among analytical methods applied for identification, structural characterization and quantitative analysis of the natural products. Methods of analysis of differently substituted flavonoids (O- and C-glycosides, differentiation of various oligosaccharidic substituents, detection of acylated compounds) are presented in the paper. A proper application of mass spectrometric methods in well-defined and strictly controlled technical parameters of analysis permits obtaining important structural information. Among others, recording collision induced dissociation mass spectra allows identification of compounds after comparison of the registered MS spectra with these present in the existing databases.     

Comprehensive mass spectrometry‐guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large‐scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time‐feasible manner. Here we introduce a workflow for large‐scale, semi‐automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well‐known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co‐occurring fragments and neutral losses, so‐called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7‐O‐methyltransferase activity. Our workflow provides the foundations for the automated, high‐throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.     

New styrylpyrones from the fungal endophyte Penicillium glabrum isolated from Punica granatum

The endophytic fungus Penicillium glabrum was isolated from pomegranate fruits (Punica granatum) collected in Uzbekistan. Extracts of the fungus grown on rice yielded two new styrylpyrones, namely 3-methyldesmethoxyyangonin (1) and 3-methylbisnoryangonin (2), together with four known metabolites. The structures of the isolated compounds were elucidated on the basis of comprehensive spectral analysis (1D and 2D NMR and MS).     

A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.