Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

Hemoglobin (Hb) constitutes an important protein in clinical diagnosticsboth in humans and animals. Among the high number of sequence variants, some can cause severe diseases. Moreover, chemical modifications such as glycation and carbamylation serve as important biomarkers for conditions such as diabetes and kidney diseases. In clinical routine analysis of glycated Hb, sequence variants or other Hb proteoforms can cause interference, resulting in wrong quantification results. We present a versatile and flexible capillary zone electrophoresis-mass spectrometry screening method for Hb proteoforms including sequence variants and modified species extracted from dried blood spot (DBS) samples with virtually no sample preparation. High separation power was achieved by application of a 5-layers successive multiple ionic polymer layers-coated capillary, enabling separation of positional isomers of glycated α- and β-chains on the intact level. Quantification of glycated Hb was in good correlation with the results obtained in a clinical routine method. Identification and characterization of known and unknown proteoforms was performed by fragmentation of intact precursor ions. N-Terminal and lysine glycation could be identified on the α- and β-chain, respectively. The versatility of the method was demonstrated by application to dog and cat DBS samples. We discovered a putative new sequence variant of the β-chain in dog (T38 → A). The presented method enables separation, characterization, and quantification of intact proteoforms, including positional isomers of glycated species in a single run. Combined with the simple sample preparation, our method represents a valuable tool to be used for deeper characterization of clinical and veterinary samples.

Pyrrolizidine alkaloids and their corresponding pyrrolizidine alkaloid-N-oxides are secondary plant constituents that became the subject of public concern due to their hepatotoxic, pneumotoxic, genotoxic, and cytotoxic effects. In contrast to the well-established analytical separation and detection methods, only a few studies have investigated the extraction of pyrrolizidine alkaloids/pyrrolizidine alkaloid-N-oxides from plant material. In this study, we have applied pressurized liquid extraction with the aim of evaluating the effect of various parameters on the recovery of pyrrolizidine alkaloids. The nature of the modifier (various acids, NH3) added to the aqueous extraction solvent, its concentration (1 or 5%), and the temperature (50 – 125 °C) were systematically varied. To analyse a wide range of structurally different pyrrolizidine alkaloids, Jacobaea vulgaris (syn. Senecio jacobaea), Tussilago farfara, and Symphytum officinale were included. Pyrrolizidine alkaloids were quantified by HPLC-MS/MS and the results obtained by pressurised liquid extraction were compared with the amount of pyrrolizidine alkaloids determined by an official reference method. Using this approach, increased rates of recovery were obtained for J. vulgaris (up to 174.4%), T. farfara (up to 156.5%), and S. officinale (up to 288.7%). Hence, pressurised liquid extraction was found to be a promising strategy for the complete and automated extraction of pyrrolizidine alkaloids, which could advantageously replace other time- and solvent-consuming extraction methods.

Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.

Introduction Polar metabolites in Caenorhabditis elegans (C. elegans) have predominantly been analyzed using hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC-MS). Capillary electrophoresis coupled to mass spectrometry (CE-MS) represents another complementary analytical platform suitable for polar and charged analytes. Objective We compared CE-MS and HILIC-MS for the analysis of a set of 60 reference standards relevant for C. elegans and specifically investigated the strengths of CE separation. Furthermore, we employed CE-MS as a complementary analytical approach to study polar metabolites in C. elegans samples, particularly in the context of longevity, in order to address a different part of its metabolome. Method We analyzed 60 reference standards as well as metabolite extracts from C. elegans daf-2 loss-of-function mutants and wild-type (WT) samples using HILIC-MS and CE-MS employing a Q-ToF-MS instrument. Results CE separations showed narrower peak widths and a better linearity of the estimated response function across different concentrations which is linked to less saturation of the MS signals. Additionally, CE exhibited a distinct selectivity in the separation of compounds compared to HILIC-MS, providing complementary information for the analysis of the target compounds. Analysis of C. elegans metabolites of daf-2 mutants and WT samples revealed significant alterations in shared metabolites identified through HILIC-MS, as well as the presence of distinct metabolites. Conclusion CE-MS was successfully applied in C. elegans metabolomics, being able to recover known as well as identify novel putative biomarkers of longevity.

Summary We present MobilityTransformR, an R/Bioconductor package for the effective mobility scaling of capillary zone electrophoresis-mass spectrometry (CE-MS) data. It uses functionality from different R packages that are frequently used for data processing and analysis in MS-based metabolomics workflows, allowing the subsequent use of reproducible transformed CE-MS data in existing workflows. Availability and Implementation MobilityTransformR is implemented in R (Version > = 4.2) and can be downloaded directly from the Bioconductor database (https://bioconductor.org/packages/MobilityTransformR) or GitHub (https://github.com/LiesaSalzer/MobilityTransformR). Supplementary information Supplementary data are available at Bioinformatics online.

No abstract