ALLocator: An Interactive Web Platform for the Analysis of Metabolomic LC-ESI-MS Datasets, Enabling Semi-Automated, User-Revised Compound Annotation and Mass Isotopomer Ratio Analysis

Kessler N, Walter F, Persicke M, et al. ALLocator: An Interactive Web Platform for the Analysis of Metabolomic LC-ESI-MS Datasets, Enabling Semi-Automated, User-Revised Compound Annotation and Mass Isotopomer Ratio Analysis. PLoS ONE. 2014;9(11): e113909.Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution “ALLocatorSD” generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains. ALLocator is available online at: https://allocator.cebitec.uni-bielefeld.​de. A login is required, but freely available

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

Structure analysis of biologically important prokaryotic glycopolymers

Of the many post-translational modifications organisms can undertake, glycosylation is the most prevalent and the most diverse. The research in this thesis focuses on the structural characterisation of glycosylation in two classes of glycopolymer (lipopolysaccharide (LPS) and glycoprotein) in two domains of life (bacteria and archaea). The common theme linking these subprojects is the development and application of high sensitivity analytical techniques, primarily mass spectrometry (MS), for studying prokaryotic glycosylation. Many prokaryotes produce glycan arrangements with extraordinary variety in composition and structure. A further challenge is posed by additional functionalities such as lipids whose characterisation is not always straightforward. Glycosylation in prokaryotes has a variety of different biological functions, including their important roles in the mediation of interactions between pathogens and hosts. Thus enhanced knowledge of bacterial glycosylation may be of therapeutic value, whilst a better understanding of archaeal protein glycosylation will provide further targets for industrial applications, as well as insight into this post- translational modification across evolution and protein processing under extreme conditions. The first sub-project focused on the S-layer glycoprotein of the halophilic archeaon Haloferax volcanii, which has been reported to be modified by both glycans and lipids. Glycoproteomic and associated MS technologies were employed to characterise the N- and O-linked glycosylation and to explore putative lipid modifications. Approximately 90% of the S-layer was mapped and N-glycans were identified at all the mapped consensus sites, decorated with a pentasaccharide consisting of two hexoses, two hexuronic acids and a methylated hexuronic acid. The O-glycans are homogeneously identified as a disaccharide consisting of galactose and glucose. Unexpectedly it was found that membrane-derived lipids were present in the S- layer samples despite extensive purification, calling into question the predicted presence of covalently linked lipid. The H. volcanii N-glycosylation is mediated by the products of the agl gene cluster and the functional characterisation of members of the agl gene cluster was investigated by MS analysis of agl-mutant strains of the S-layer. Burkholderia pseudomallei is the causative agent of melioidosis, a serious and often fatal disease in humans which is endemic in South-East Asia and other equatorial regions. Its LPS is vital for serum resistance and the O-antigen repeat structures are of interest as vaccine targets. B. pseudomallei is reported to produce several polysaccharides, amongst which the already characterised ‘typical’ O-antigen of K96243 represents 97% of the strains. The serologically distinct ‘atypical’ strain 576 produces a different LPS, whose characterisation is the subject of this research project. MS strategies coupled with various hydrolytic and chemical derivatisation methodologies were employed to define the composition and potential sequences of the O-antigen repeat unit. These MS strategies were complemented by a novel NMR technique involving embedding of the LPS into micelles. Taken together the MS and NMR data have revealed a highly unusual O-antigen structure for atypical LPS which is remarkably different from the typical O-antigen. The development of structural analysis tools in MS and NMR applicable to the illustrated types of glycosylation in these prokaryotes will give a more consistent approach to sugar characterisation and their modifications thus providing more informative results for pathogenicity and immunological studies as well as pathway comparisons.Open Acces

Metabolic profiling of human plasma and urine in chronic kidney disease by hydrophilic interaction liquid chromatography coupled with time-of-flight mass spectrometry : a pilot study

A typical characteristic of chronic kidney disease (CKD) is the progressive loss in renal function over a period of months or years with the concomitant accumulation of uremic retention solutes in the body. Known biomarkers for the kidney deterioration, such as serum creatinine or urinary albumin, do not allow effective early detection of CKD, which is essential towards disease management. In this work, a hydrophilic interaction liquid chromatography time-of-flight mass spectrometric (HILIC-TOF MS) platform was optimized allowing the search for novel uremic retention solutes and/or biomarkers of CKD. The HILIC-ESI-MS approach was used for the comparison of urine and plasma samples from CKD patients at stage 3 (n = 20), at stage 5 not yet receiving dialysis (n = 20) and from healthy controls (n = 20). Quality control samples were used to control and ensure the validity of the metabolomics approach. Subsequently the data were treated with the XCMS software for multivariate statistical analysis. In this way, differentiation could be achieved between the measured metabolite profile of the CKD patients versus the healthy controls. The approach allowed the elucidation of a number of metabolites that showed a significant up- and downregulation throughout the different stages of CKD. These compounds are cinnamoylglycine, glycoursodeoxycholic acid, 2-hydroxyethane sulfonate, and pregnenolone sulfate of which the identity was unambiguously confirmed via the use of authentic standards. The latter three are newly identified uremic retention solutes

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Non-targeted LC-MS based metabolomics analysis of the urinary steroidal profile

The urinary steroidal fraction has been extensively explored as non-invasive alternative to monitor pathological conditions as well as to unveil the illicit intake of pseudo-endogenous anabolic steroids in sport. However, the majority of previous approaches involved the a priori selection of potentially relevant target analytes. Here we describe the non-targeted analysis of the urinary steroidal profiles. The workflow includes minimal sample pretreatment and normalization according to the specific gravity of urine, a 20 min reverse phase ultra-performance liquid chromatographic separation hyphenated to electrospray time-of-flight mass spectrometry. As initial validation, we analyzed a set of quality control urines spiked with glucurono- and sulfo-conjugated steroids at physiological ranges. We then applied the method for the analysis of samples collected after single transdermal administration of testosterone in hypogonadal men. The method allowed profiling of approximately three thousand metabolic features, including steroids of clinical and forensic relevance. It successfully identified metabolic pathways mostly responsible for groups clustering even in the context of high inter-individual variability and allowed the detection of currently unknown metabolic features correlating with testosterone administration. These outcomes set the stage for future studies aimed at implementing currently monitored urinary steroidal markers both in clinical and forensic analysis

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). RESULTS: Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. CONCLUSION: We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics.

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics