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

Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed

Metabolomics based annotation of novel genes in Arabidopsis thaliana

Of the approximately 30,000 genes that are predicted to be in the Arabidopsis genome, the function of a large proportion is experimentally undetermined. The NSF Arabidopsis 2010 program has supported the development of community resources to identify the functions of these genes. As part of this effort, a consortium of metabolomics laboratories has been established to generate and evaluate metabolomics data as a tool for generating hypotheses concerning the metabolic and physiological function of genes of unknown function. In combination, this consortium has the ability to evaluate the relative abundance of nearly 2,000 Arabidopsis metabolites. This consortium is combining reverse genetics and metabolomics to evaluate the consequence of loss-of-function mutations on the Arabidopsis metabolome and provide information to formulate initial hypotheses about genes of unknown function that can be further explored by the research community

Comparative metabolomics in primates reveals the effects of diet and gene regulatory variation on metabolic divergence.

Human diets differ from those of non-human primates. Among few obvious differences, humans consume more meat than most non-human primates and regularly cook their food. It is hypothesized that a dietary shift during human evolution has been accompanied by molecular adaptations in metabolic pathways. Consistent with this notion, comparative studies of gene expression levels in primates have found that the regulation of genes with metabolic functions tend to evolve rapidly in the human lineage. The metabolic consequences of these regulatory differences, however, remained unknown. To address this gap, we performed a comparative study using a combination of gene expression and metabolomic profiling in livers from humans, chimpanzees, and rhesus macaques. We show that dietary differences between species have a strong effect on metabolic concentrations. In addition, we found that differences in metabolic concentration across species are correlated with inter-species differences in the expression of the corresponding enzymes, which control the same metabolic reaction. We identified a number of metabolic compounds with lineage-specific profiles, including examples of human-species metabolic differences that may be directly related to dietary differences

Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99△raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99△raxST. LC–TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC–TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99△raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions

How Large Is the Metabolome? A Critical Analysis of Data Exchange Practices in Chemistry

Calculating the metabolome size of species by genome-guided reconstruction of metabolic pathways misses all products from orphan genes and from enzymes lacking annotated genes. Hence, metabolomes need to be determined experimentally. Annotations by mass spectrometry would greatly benefit if peer-reviewed public databases could be queried to compile target lists of structures that already have been reported for a given species. We detail current obstacles to compile such a knowledge base of metabolites.As an example, results are presented for rice. Two rice (oryza sativa) subspecies have been fully sequenced, oryza japonica and oryza indica. Several major small molecule databases were compared for listing known rice metabolites comprising PubChem, Chemical Abstracts, Beilstein, Patent databases, Dictionary of Natural Products, SetupX/BinBase, KNApSAcK DB, and finally those databases which were obtained by computational approaches, i.e. RiceCyc, KEGG, and Reactome. More than 5,000 small molecules were retrieved when searching these databases. Unfortunately, most often, genuine rice metabolites were retrieved together with non-metabolite database entries such as pesticides. Overlaps from database compound lists were very difficult to compare because structures were either not encoded in machine-readable format or because compound identifiers were not cross-referenced between databases.We conclude that present databases are not capable of comprehensively retrieving all known metabolites. Metabolome lists are yet mostly restricted to genome-reconstructed pathways. We suggest that providers of (bio)chemical databases enrich their database identifiers to PubChem IDs and InChIKeys to enable cross-database queries. In addition, peer-reviewed journal repositories need to mandate submission of structures and spectra in machine readable format to allow automated semantic annotation of articles containing chemical structures. Such changes in publication standards and database architectures will enable researchers to compile current knowledge about the metabolome of species, which may extend to derived information such as spectral libraries, organ-specific metabolites, and cross-study comparisons

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results