Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

/pdf/metaboanalyst-5-0-narrowing-the-gap-between-raw-spectra-and-uhscoef1c4.pdf

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

First released in 2009, MetaboAnalyst (wwwmetaboanalystca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to the original server was necessary MetaboAnalyst 20, which is the successor to MetaboAnalyst, represents just such an upgrade MetaboAnalyst 20 now contains dozens of new features and functions including new procedures for data filtering, data editing and data normalization It also supports multi-group data analysis, two-factor analysis as well as time-series data analysis These new functions have also been supplemented with: (i) a quality-control module that allows users to evaluate their data quality before conducting any analysis, (ii) a functional enrichment analysis module that allows users to identify biologically meaningful patterns using metabolite set enrichment analysis and (iii) a metabolic pathway analysis module that allows users to perform pathway analysis and visualization for 15 different model organisms In developing MetaboAnalyst 20 we have also substantially improved its graphical presentation tools All images are now generated using anti-aliasing and are available over a range of resolutions, sizes and formats (PNG, TIFF, PDF, PostScript, or SVG) To improve its performance, MetaboAnalyst 20 is now hosted on a much more powerful server with substantially modified code to take advantage the server’s multi-core CPUs for computationally intensive tasks MetaboAnalyst 20 also maintains a collection of 50 or more FAQs and more than a dozen tutorials compiled from user queries and requests A downloadable version of MetaboAnalyst 20, along detailed instructions for local installation is now available as well

/pdf/metaboanalyst-2-0-a-comprehensive-server-for-metabolomic-5fi0ibvq2k.pdf

MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis

Emerging evidence has linked the gut microbiome to human obesity. We performed a metagenome-wide association study and serum metabolomics profiling in a cohort of lean and obese, young, Chinese individuals. We identified obesity-associated gut microbial species linked to changes in circulating metabolites. The abundance of Bacteroides thetaiotaomicron, a glutamate-fermenting commensal, was markedly decreased in obese individuals and was inversely correlated with serum glutamate concentration. Consistently, gavage with B. thetaiotaomicron reduced plasma glutamate concentration and alleviated diet-induced body-weight gain and adiposity in mice. Furthermore, weight-loss intervention by bariatric surgery partially reversed obesity-associated microbial and metabolic alterations in obese individuals, including the decreased abundance of B. thetaiotaomicron and the elevated serum glutamate concentration. Our findings identify previously unknown links between intestinal microbiota alterations, circulating amino acids and obesity, suggesting that it may be possible to intervene in obesity by targeting the gut microbiota.

Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention

Urine metabolomics is widely used for biomarker research in the fields of medicine and toxicology. As a consequence, characterization of the variations of the urine metabolome under basal conditions becomes critical in order to avoid confounding effects in cohort studies. Such physiological information is however very scarce in the literature and in metabolomics databases so far. 
Here we studied the influence of age, body mass index (BMI), and gender on metabolite concentrations in a large cohort of 183 adults by using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). We implemented a comprehensive statistical workflow for univariate hypothesis testing and modeling by orthogonal partial least-squares (OPLS), which we made available to the metabolomics community within the online Workflow4Metabolomics.org resource. 
We found 108 urine metabolites displaying concentration variations with either age, BMI, or gender, by integrating the results from univariate p-values and multivariate variable importance in projection (VIP). Several metabolite clusters were further evidenced by correlation analysis, and they allowed stratification of the cohort. In conclusion, our study highlights the impact of gender and age on the urinary metabolome, and thus it indicates that these factors should be taken into account for the design of metabolomics studies.

Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ∼48 h: 1 h for system setup, 8–10 h for sample preparation, 34 h for UPLC-MS analysis and 2–3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Global metabolic profiling of animal and human tissues via UPLC-MS

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

http://dbkweb.mib.man.ac.uk/pubs/pdfs/Dunn-NatProt2011.pdf

Nadine Anderson

Papers

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry