Untargeted Metabolomics Workflow Research Articles

Chemical Profile of Ocotea delicata (Lauraceae) Using Ultra High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry-Global Natural Products Social Molecular Networking Workflow.

Ocotea, the largest genus in the Lauraceae family, encompasses numerous species of scientific interest. However, most Ocotea species have only been described morphologically. This study used an untargeted metabolomics workflow with UHPLC-HRMS and GNPS-FBMN to provide the first chemical evaluation of the polar specialized metabolites of O. delicata leaves. Leaves from three O. delicata specimens were extracted using ultrasound-assisted extraction with 70% ethanol. Among the examined samples, 44 metabolites, including alkaloids and flavonoids, were identified. In contrast to other Ocotea species, O. delicata has a wider diversity of kaempferol derivatives than quercetin. The biomass of the specimens showed a significant correlation with the chemical profile. The similarity among specimens was mostly determined by the concentrations of quinic acid, kaempferol glycosides, and boldine. The evaluated specimens exhibited chemical features similar to those of species classified as New World Ocotea, with the coexistence of aporphine and benzylisoquinoline alkaloids.

Discovery of Glycation Products: Unraveling the Unknown Glycation Space Using a Mass Spectral Library from In Vitro Model Systems.

The nonenzymatic reaction between amino acids (AAs) and reducing sugars, also known as the Maillard reaction, is the primary source of free glycation products (GPs) in vivo and in vitro. The limited number of MS/MS records for GPs in public libraries hinders the annotation and investigation of nonenzymatic glycation. To address this issue, we present a mass spectral library containing the experimental MS/MS spectra of diverse GPs from model systems. Based on the conceptional reaction processes and structural characteristics of products, we classified GPs into common GPs (CGPs) and modified AAs (MAAs). A workflow for annotating GPs was established based on the structural and fragmentation patterns of each GP type. The final spectral library contains 157 CGPs, 499 MAAs, and 2426 GP spectra with synthetic model system information, retention time, precursor m/z, MS/MS, and annotations. As a proof-of-concept, we demonstrated the use of the library for screening GPs in unidentified spectra of human plasma and urine. The AAs with the C6H10O5 modification, fructosylation from Amadori rearrangement, were the most found GPs. With the help of the model system, we confirmed the existence of C6H10O5-modified Valine in human plasma by matching both retention time, MS1, and MS/MS without reference standards. In summary, our GP library can serve as an online resource to quickly screen possible GPs in an untargeted metabolomics workflow, furthermore with the model system as a practical synthesis method to confirm their identity.

Comprehensive metabolome characterization and comparison between two sources of Dragon's blood by integrating liquid chromatography/mass spectrometry and chemometrics.

Dragon's Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results beingbeneficial to quality control and thedifferentiated clinical application of DB.

Untargeted Maternal Plasma Metabolomics in Hirschsprung Disease: A Pilot Study.

Hirschsprung disease (HSCR) is a congenital disorder of unknown etiology affecting the enteric nervous system (ENS). Since the early gestational development of the ENS is dependent on the prenatal maternal metabolic environment, the objective of this pilot study was to explore the role of specific maternal plasma metabolites in the etiology of HSCR. In this cross-sectional study, postnatal (as a surrogate for prenatal) plasma samples were obtained from mothers of children diagnosed with HSCR (n = 7) and age-matched mothers of normal children (n = 6). The plasma metabolome was analyzed by ultra-high-pressure liquid chromatography and mass spectrometry. Metabolites were identified by mzCloud using Compound Discoverer software. Using an untargeted metabolomics workflow, metabolites with case versus control group differences were identified. A total of 268 unique plasma metabolites were identified and annotated in maternal plasma. Of these, 57 were significantly different between case and control groups (P < 0.05, t-test). Using a false discovery rate corrected cutoff of 10% to adjust for multiple comparisons, 19 metabolites were significantly different in HSCR cases, including carnitines, medium-chain fatty acids, and glutamic acid. Pathways affected were for amino acid and lipid metabolism. Disordered prenatal metabolic pathways may be involved in the etiopathogenesis of HSCR in the developing fetus. This is the first study to assess maternal plasma metabolomics in HSCR.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics.

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.Graphical

Unravelling the seasonal dynamics of the metabolome of white asparagus spears using untargeted metabolomics

IntroductionThe white asparagus season lasts 4 months while the harvest period per field is 8 weeks. Different varieties are better suited for harvesting early or late in the season. Little is known of the dynamics of secondary metabolites of white asparagus during the production season.ObjectiveCharacterization of the metabolome of white asparagus spears covering volatile and non-volatile composition in relation to quality aspects.MethodsEight varieties, harvested repeatedly during two consecutive seasons were analysed following an untargeted metabolomics workflow using SPME GC–MS and LC–MS. Linear regression, cluster and network analyses were used to explore the profile dynamics, unravel patterns and study the influence of genotype and environment.ResultsThe metabolite profiles were influenced by the harvest moment and genetic background. Metabolites that significantly changed over time were distributed across seven clusters based on their temporal patterns. Two clusters including monoterpenes, benzenoids and saponins showed the most prominent seasonal changes. The changes depicted by the other five clusters were mainly ≤ 2-fold relative to the harvest start. Known asparagus aroma compounds were found to be relatively stable across the season/varieties. Heat-enhanced cultivation appeared to yield spears early in season with a similar metabolome to those harvested later.ConclusionThe dynamics of the white asparagus metabolome is influenced by a complex relationship between the onset of spear development, the moment of harvest and the genetic background. The typical perceived asparagus flavour profile is unlikely to be significantly affected by these dynamics.

Untargeted LC-HRMS metabolomics reveals candidate biomarkers for mucopolysaccharidoses

BackgroundMucopolysaccharidoses (MPSs) are inherited genetic diseases caused by an absence or deficiency of lysosomal enzymes responsible for catabolizing glycosaminoglycans (GAGs). Undiagnosed patients, or those without adequate treatment in early life, can be severely and irreversibly affected by the disease. In this study, we applied liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to identify potential biomarkers for MPS disorders to better understand how MPS may affect the metabolome of patients. MethodsUrine samples from 37 MPS patients (types I, II, III, IV, and VI; untreated and treated with enzyme replacement therapy (ERT)) and 38 controls were analyzed by LC-HRMS. Data were processed by an untargeted metabolomics workflow and submitted to multivariate statistical analyses to reveal significant differences between the MPS and control groups. ResultsA total of 12 increased metabolites common to all MPS types were identified. Dipeptides, amino acids and derivatives were increased in the MPS group compared to controls. N-acetylgalactosamines 4- or 6-sulfate, important constituents of GAGs, were also elevated in MPS patients, most prominently in those with MPS VI. Notably, treated patients exhibited lower levels of the aforementioned acylaminosugars than untreated patients in all MPS types. ConclusionsUntargeted metabolomics has enabled the detection of metabolites that could improve our understanding of MPS physiopathology. These potential biomarkers can be utilized in screening methods to support diagnosis and ERT monitoring.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies.

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

In Vitro and In Vivo Toxicometabolomics of the Synthetic Cathinone PCYP Studied by Means of LC-HRMS/MS.

Synthetic cathinones are one important group amongst new psychoactive substances (NPS) and limited information is available regarding their toxicokinetics and -dynamics. Over the past few years, nontargeted toxicometabolomics has been increasingly used to study compound-related effects of NPS to identify important exogenous and endogenous biomarkers. In this study, the effects of the synthetic cathinone PCYP (2-cyclohexyl-1-phenyl-2-(1-pyrrolidinyl)-ethanone) on in vitro and in vivo metabolomes were investigated. Pooled human-liver microsomes and blood and urine of male Wistar rats were used to generate in vitro and in vivo data, respectively. Samples were analyzed by liquid chromatography and high-resolution mass spectrometry using an untargeted metabolomics workflow. Statistical evaluation was performed using univariate and multivariate statistics. In total, sixteen phase I and one phase II metabolite of PCYP could be identified as exogenous biomarkers. Five endogenous biomarkers (e.g., adenosine and metabolites of tryptophan metabolism) related to PCYP intake could be identified in rat samples. The present data on the exogenous biomarker of PCYP are crucial for setting up analytical screening procedures. The data on the endogenous biomarker are important for further studies to better understand the physiological changes associated with cathinone abuse but may also serve in the future as additional markers for an intake.

Discovery of Food Intake Biomarkers Using Metabolomics.

Due to the high impact of diet exposure on health, it is crucial the generation of robust data of regular dietary intake, hence improving the accuracy of dietary assessment. The metabolites derived from individual food or group of food have great potential to become biomarkers of food intake (BFIs) and provide more objective food consumption measurements.Herein, it is presented an untargeted metabolomic workflow for the discovery BFIs in blood and urine samples, from the study design to the biomarker identification. Samples are analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). A wide variety of compounds are covered by separate analyses of medium to nonpolar molecules and polar metabolites based on two LC separations as well as both positive and negative electrospray ionization. The main steps of data treatment of the comprehensive data sets and statistical analysis are described, as well as the principal considerations for the BFI identification.

Rapid HILIC-Z ion mobility mass spectrometry (RHIMMS) method for untargeted metabolomics of complex biological samples

IntroductionRecent advances in high-throughput methodologies in the ‘omics’ and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples.ObjectiveThe objective of this research was to evaluate a straightforward to implement LC–MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored.MethodsA rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome.ResultsThe method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds.ConclusionOur results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.

Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features

Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.Graphical

Headspace Solid-Phase Micro-Extraction Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS)-Based Untargeted Metabolomics Analysis for Comparing the Volatile Components from 12 Panax Herbal Medicines

Quality control of ginseng currently is mainly based on ginsenoside analysis, but rarely focuses on the volatile organic components. In the current work, an untargeted metabolomics approach, by headspace solid-phase micro-extraction gas chromatography/mass spectrometry (HS-SPME-GC/MS), was elaborated and further employed to holistically compare the compositional difference of the volatile components simultaneously from 12 Panax herbal medicines, which included P. ginseng (PG), P. quinquefolius (PQ), P. notoginseng (PN), red ginseng (PGR), P. ginseng leaf (PGL), P. quinquefolius leaf (PQL), P. notoginseng leaf (PNL), P. ginseng flower (PGF), P. quinquefolius flower (PQF), P. notoginseng flower (PNF), P. japonicus (PJ), and P. japonicus var. major (PJvm). Chromatographic separation was performed on an HP-5MS elastic quartz capillary column using helium as the carrier gas, enabling good resolution within 1 h. We were able to characterize totally 259 volatile compounds, including 82 terpenes (T), 46 alcohols (Alc), 29 ketones (K), 25 aldehydes (Ald), 21 esters (E), and the others. By analyzing 90 batches of ginseng samples based on the untargeted metabolomics workflows, 236 differential ions were unveiled, and accordingly 36 differential volatile components were discovered. It is the first report that simultaneously compares the compositional difference of volatile components among 12 Panax herbal medicines, and useful information is provided for the quality control of ginseng aside from the well-known ginsenosides.

An optimized LC-HRMS untargeted metabolomics workflow for multi-matrices investigations in the three-spined stickleback.

Environmental metabolomics has become a growing research field to understand biological and biochemical perturbations of organisms in response to various abiotic or biotic stresses. It focuses on the comprehensive and systematic analysis of a biologic system’s metabolome. This allows the recognition of biochemical pathways impacted by a stressor, and the identification of some metabolites as biomarkers of potential perturbations occurring in a body. In this work, we describe the development and optimization of a complete reliable methodology based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) for untargeted metabolomics studies within a fish model species, the three-spined stickleback (Gasterosteus aculeatus). We evaluated the differences and also the complementarities between four different matrices (brain, gills, liver and whole fish) to obtain metabolome information. To this end, we optimized and compared sample preparation and the analytical method, since the type and number of metabolites detected in any matrix are closely related to these latter. For the sample preparation, a solid-liquid extraction was performed on a low quantity of whole fish, liver, brain, or gills tissues using combinations of methanol/water/heptane. Based on the numbers of features observed in LC-HRMS and on the responses of analytical standards representative of different metabolites groups (amino acids, sugars…), we discuss the influence of the nature, volume, and ratio of extraction solvents, the sample weight, and the reconstitution solvent. Moreover, the analytical conditions (LC columns, pH and additive of mobile phases and ionization modes) were also optimized so as to ensure the maximum metabolome coverages. Thus, two complementary chromatographic procedures were combined in order to cover a broader range of metabolites: a reversed phase separation (RPLC) on a C18 column followed by detection with positive ionization mode (ESI+) and a hydrophilic interaction chromatography (HILIC) on a zwitterionic column followed by detection with negative ionization mode (ESI-). This work provides information on brain, gills, liver, vs the whole body contribution to the stickleback metabolome. These information would help to guide ecotoxicological and biomonitoring studies.

Metabolomics Reveals Heterogeneity in the Chemical Composition of Green and White Spears of Asparagus (A. officinalis).

Green and white asparagus are quite different crops but can be harvested from the same plant. They have distinct morphological differences due to their mode of cultivation and they are characterised by having contrasting appearance and flavour. Significant chemical differences are therefore expected. Spears from three varieties of both green and white forms, harvested in two consecutive seasons were analysed using headspace GC-MS and LC-MS with an untargeted metabolomic workflow. Mainly C5 and C8 alcohols and aldehydes, and phenolic compounds were more abundant in green spears, whereas benzenoids, monoterpenes, unsaturated aldehydes and steroidal saponins were more abundant in white ones. Previously reported key asparagus volatiles and non-volatiles were detected at similar or not significantly different levels in the two asparagus types. Spatial metabolomics revealed also that many volatiles with known positive aroma attributes were significantly more abundant in the upper parts of the spears and showed a decreasing trend towards the base. These findings provide valuable insights into the metabolome of raw asparagus, the contrasts between green and white spears as well as the different chemical distributions along the stem.

Altered metabolomic states elicited by Flg22 and FlgII-28 in Solanum lycopersicum: intracellular perturbations and metabolite defenses

BackgroundSurveillance of potential pathogens is a key feature of plant innate immunity. For non-self-recognition plants rely on the perception of pathogen-derived molecules. Early post-perception events activate signaling cascades, leading to the synthesis of defense-related proteins and specialized metabolites, thereby providing a broad-spectrum antimicrobial coverage. This study was concerned with tracking changes in the tomato plant metabolome following perception of the flagellum-derived elicitors (Flg22 and FlgII-28).ResultsFollowing an untargeted metabolomics workflow, the metabolic profiles of a Solanum lycopersicum cultivar were monitored over a time range of 16–32 h post-treatment. Liquid chromatography was used to resolve the complex mixture of metabolites and mass spectrometry for the detection of differences associated with the elicitor treatments. Stringent data processing and multivariate statistical tools were applied to the complex dataset to extract relevant metabolite features associated with the elicitor treatments. Following perception of Flg22 and FlgII-28, both elicitors triggered an oxidative burst, albeit with different kinetic responses. Signatory biomarkers were annotated from diverse metabolite classes which included amino acid derivatives, lipid species, steroidal glycoalkaloids, hydroxybenzoic acids, hydroxycinnamic acids and derivatives, as well as flavonoids.ConclusionsAn untargeted metabolomics approach adequately captured the subtle and nuanced perturbations associated with elicitor-linked plant defense responses. The shared and unique features characterizing the metabolite profiles suggest a divergence of signal transduction events following perception of Flg22 vs. FlgII-28, leading to a differential reorganization of downstream metabolic pathways.

Untargeted Metabolomics for the Diagnosis of Exocrine Pancreatic Insufficiency in Chronic Pancreatitis.

Background and Objectives: The clinical manifestations and course of chronic pancreatitis (CP) are often nonspecific and variable, hampering diagnosis of the risk of exocrine pancreatic insufficiency (EPI). Development of new, reproducible, and non-invasive methods to diagnose EPI is therefore a major priority. The objective of this metabolomic study was to identify novel biomarkers associated with EPI. Materials and Methods: We analyzed 53 samples from patients with CP, 32 with and 21 without EPI, using an untargeted metabolomics workflow based on hydrophilic interaction chromatography coupled to high-resolution mass spectrometry. Principal component and partial least squares-discriminant analyses showed significant between-group differentiation, and univariate and multivariate analyses identified potential candidate metabolites that significantly differed between samples from CP patients with EPI and those without EPI. Results: Excellent results were obtained using a six-metabolic panel to diagnose the presence of EPI in CP patients (area under the ROC curve = 0.785). Conclusions: This study confirms the usefulness of metabolomics in this disease setting, allowing the identification of novel biomarkers to differentiate between the presence and absence of EPI in CP patients.

A Robust Method for Sample Preparation of Gastrointestinal Stromal Tumour for LC/MS Untargeted Metabolomics

Gastrointestinal stromal tumour has already been well explored at the genome level; however, little is known about metabolic processes occurring in the sarcoma. Sample preparation is a crucial step in untargeted metabolomics workflow, highly affecting the metabolome coverage and the quality of the results. In this study, four liquid-liquid extraction methods for the isolation of endogenous compounds from gastrointestinal stromal tumours were compared and evaluated. The protocols covered two-step or stepwise extraction with methyl-tert-butyl ether (MTBE) or dichloromethane. The extracts were subjected to LC-MS analysis by the application of reversed-phase and hydrophilic interaction liquid chromatography to enable the separation and detection of both polar and nonpolar analytes. The extraction methods were compared in terms of efficiency (total number of detected metabolites) and reproducibility. The method was based on the stepwise extraction with MTBE, methanol, and water proved to be the most reproducible, and thus, its robustness to fluctuations in experimental conditions was assessed employing Plackett–Burman design and hierarchical modelling. While most studied factors had no effect on the metabolite abundance, the highest coefficient value was observed for the volume of MTBE added during extraction. Herein, we demonstrate the application and the feasibility of the selected protocol for the analysis of gastrointestinal stromal tumour samples. The method selected could be considered as a reference for the best characterization of underlying molecular changes associated with complex tissue extracts of GIST.

Chemical labeling strategies for small molecule natural product detection and isolation.

Covering: Up to 2020.It is widely accepted that small molecule natural products (NPs) evolved to carry out a particular ecological function and that these finely-tuned molecules can sometimes be appropriated for the treatment of disease in humans. Unfortunately, for the natural products chemist, NPs did not evolve to possess favorable physicochemical properties needed for HPLC-MS analysis. The process known as derivatization, whereby an NP in a complex mixture is decorated with a nonnatural moiety using a derivatizing agent (DA), arose from this sad state of affairs. Here, NPs are freed from the limitations of natural functionality and endowed, usually with some degree of chemoselectivity, with additional structural features that make HPLC-MS analysis more informative. DAs that selectively label amines, carboxylic acids, alcohols, phenols, thiols, ketones, and aldehydes, terminal alkynes, electrophiles, conjugated alkenes, and isocyanides have been developed and will be discussed here in detail. Although usually employed for targeted metabolomics, chemical labeling strategies have been effectively applied to uncharacterized NP extracts and may play an increasing role in the detection and isolation of certain classes of NPs in the future.