Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary data are available at Bioinformatics online

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites

A Workflow for the Identification of Mycotoxin Metabolites Using Liquid Chromatography–Ion Mobility-Mass Spectrometry

The structural identification of phase-I and phase-II metabolites of mycotoxins is a difficult task, mostly due to the lack of standards and because of the large number of isomeric forms. Here, we describe the use of ion mobility-mass spectrometry to analyze cereal extracts and how structural information on newly discovered mycotoxins metabolites could be obtained

The SLUGGS Survey: stellar kinematics, kinemetry and trends at large radii in 25 early-type galaxies

Due to longer dynamical time-scales, the outskirts of early-type galaxies retain the footprint of their formation and assembly. Under the popular two-phase galaxy formation scenario, an initial in situ phase of star formation is followed by minor merging and accretion of ex situ stars leading to the expectation of observable transitions in the kinematics and stellar populations on large scales. However, observing the faint galactic outskirts is challenging, often leaving the transition unexplored. The large-scale, spatially resolved stellar kinematic data from the SAGES Legacy Unifying Galaxies and GlobularS (SLUGGS) survey are ideal for detecting kinematic transitions. We present kinematic maps out to 2.6 effective radii on average, kinemetry profiles, measurement of kinematic twists and misalignments, and the average outer intrinsic shape of 25 SLUGGS galaxies. We find good overall agreement in the kinematic maps and kinemetry radial profiles with literature. We are able to confirm significant radial modulations in rotational versus pressure support of galaxies with radius so that the central and outer rotational properties may be quite different. We also test the suggestion that galaxies may be more triaxial in their outskirts and find that while fast rotating galaxies were already shown to be axisymmetric in their inner regions, we are unable to rule out triaxiality in their outskirts. We compare our derived outer kinematic information to model predictions from a two-phase galaxy formation scenario. We find that the theoretical range of local outer angular momentum agrees well with our observations, but that radial modulations are much smaller than predicted