Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data. These tools process, annotate, and compare MS(n) data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS(n) data of urine samples. From human urine, 30 MS(n) trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS(n) data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS(n) data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.

Fragmentation trees for the structural characterisation of metabolites.

Metabolite identification plays a crucial role in the interpretation of metabolomics research results. Due to its sensitivity and widespread implementation, a favourite analytical method used in metabolomics is electrospray mass spectrometry. In this paper, we demonstrate our results in attempting to incorporate the potentials of multistage mass spectrometry into the metabolite identification routine. New software tools were developed and implemented which facilitate the analysis of multistage mass spectra and allow for efficient removal of spectral artefacts. The pre-processed fragmentation patterns are saved as fragmentation trees. Fragmentation trees are characteristic of molecular structure. We demonstrate the reproducibility and robustness of the acquisition of such trees on a model compound. The specificity of fragmentation trees allows for distinguishing structural isomers, as shown on a pair of isomeric prostaglandins. This approach to the analysis of the multistage mass spectral characterisation of compounds is an important step towards formulating a generic metabolite identification method.

OMG: Open Molecule Generator.

Computer Assisted Structure Elucidation has been used for decades to discover the chemical structure of unknown compounds. In this work we introduce the first open source structure generator, Open Molecule Generator (OMG), which for a given elemental composition produces all non-isomorphic chemical structures that match that elemental composition. Furthermore, this structure generator can accept as additional input one or multiple non-overlapping prescribed substructures to drastically reduce the number of possible chemical structures. Being open source allows for customization and future extension of its functionality. OMG relies on a modified version of the Canonical Augmentation Path, which grows intermediate chemical structures by adding bonds and checks that at each step only unique molecules are produced. In order to benchmark the tool, we generated chemical structures for the elemental formulas and substructures of different metabolites and compared the results with a commercially available structure generator. The results obtained, i.e. the number of molecules generated, were identical for elemental compositions having only C, O and H. For elemental compositions containing C, O, H, N, P and S, OMG produces all the chemically valid molecules while the other generator produces more, yet chemically impossible, molecules. The chemical completeness of the OMG results comes at the expense of being slower than the commercial generator. In addition to being open source, OMG clearly showed the added value of constraining the solution space by using multiple prescribed substructures as input. We expect this structure generator to be useful in many fields, but to be especially of great importance for metabolomics, where identifying unknown metabolites is still a major bottleneck.

MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data.

UNLABELLED: Identification of metabolites using high-resolution multi-stage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier. AVAILABILITY: MetiTree is accessible at http://www.MetiTree.nl. The source code is available at https://github.com/NetherlandsMetabolomicsCentre/metitree/wiki.

Elemental composition determination based on MS(n).

MOTIVATION: Identification of metabolites is essential for its use as biomarkers, for research in systems biology and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High-resolution mass spectrometry, which provides the exact mass, together with common constraint rules, for rejecting false proposed elemental compositions, cannot always provide one unique elemental composition solution. RESULTS: The Multistage Elemental Formula (MEF) tool is presented in this article to enable the correct assignment of elemental composition to compounds, their fragment ions and neutral losses that originate from the molecular ion by using multistage mass spectrometry (MS(n)). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MS(n) data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially, the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MS(n) tree (the width and the depth of the tree) was addressed. This method makes an important step toward semi-automatic de novo identification of metabolites using MS(n) data. AVAILABILITY: Software available at: http://abs.lacdr.gorlaeus.net/people/rojas-cherto CONTACT: m.rojas@lacdr.leidenuniv.nl; t.reijmers@lacdr.leidenuniv.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.