Rupasri Mandal

HMDB 3.0—The Human Metabolome Database in 2013

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis

First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) 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 2.0, which is the successor to MetaboAnalyst, represents just such an upgrade. MetaboAnalyst 2.0 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 2.0 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 2.0 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 2.0 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 2.0, along detailed instructions for local installation is now available as well.

The Human Serum Metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with todays technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

The Human Urine Metabolome

Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.

YMDB: the Yeast Metabolome Database

The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated ‘metabolomic’ database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiaes importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry.

Exome Sequencing and the Management of Neurometabolic Disorders

BACKGROUND Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level. METHODS To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patients clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes. RESULTS We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%). CONCLUSIONS Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Childrens Hospital Foundation and others.).

A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows

Dairy cows fed high-grain diets during early lactation have a high incidence of metabolic disorders. However, the precise mechanism(s) of how grain feeding causes disease is not clear. In an effort to understand how this diet transition alters the rumen environment and potentially leads to certain metabolic disorders in dairy cattle, we undertook a comprehensive, quantitative metabolomic analysis of rumen fluid samples from dairy cows fed 4 different diets. Using a combination of proton nuclear magnetic resonance spectroscopy, gas chromatography-mass spectrometry, and direct flow injection tandem mass spectroscopy, we identified and quantified 93 metabolites in rumen samples taken from 8 dairy cows fed graded amounts of barley grain (i.e., 0, 15, 30, and 45% of diet dry matter). We also studied temporal changes in the rumen by studying metabolite concentration differences between the first day and the last day of each diet phase following the diet adaptation period. Multivariate analysis showed that rumen metabolites arising from the diet containing 45% barley grain were clearly different from those containing 0, 15, and 30% barley grain. Likewise, a clear separation of the metabolic composition of the ruminal fluid was evident at the beginning and at the end of each diet phase-contrary to the belief that 11 d are suitable for the adaptation of cows to high-grain diets. High-grain diets (>30%) resulted in increased rumen fluid concentrations of several toxic, inflammatory, and unnatural compounds including putrescine, methylamines, ethanolamine, and short-chain fatty acids. Perturbations in several amino acids (phenylalanine, ornithine, lysine, leucine, arginine, valine, and phenylacetylglycine) were also evident. The present study confirms and greatly extends earlier observations on dietary effects on rumen fluid composition and shows that the use of multiple metabolomic platforms permits a far more detailed understanding of metabolic causes and effects. These results may improve our understanding of diet-related rumen metabolism and the influence of grain on the overall health of dairy cattle.

ECMDB: The E. coli Metabolome Database

The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to ∼1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli’s importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry.

Metabolomics and first-trimester prediction of early-onset preeclampsia.

Objective: To evaluate the use of metabolomics for the first-trimester detection of maternal metabolic dysfunction and prediction of subsequent development of early-onset preeclampsia (PE). Study design: This was a case-control study of maternal plasma samples collected at 11–13 weeks’ gestation from 30 women who had subsequently developed PE requiring delivery before 34 weeks and 60 unaffected controls. Nuclear magnetic Resonance (NMR) spectroscopy was used to identify and quantify metabolomic changes in cases versus controls. Both genetic computing and standard statistical analyses were performed to predict the development of PE from the metabolite concentrations alone as well as the combination of metabolite concentrations with maternal characteristics and first-trimester uterine artery Doppler pulsatility index (PI). Results: Significant differences between cases and controls were found for 20 metabolites. A combination of four of these metabolites (citrate, glycerol, hydroxyisovalerate, and methionine) appeared highly predictive of PE with an estimated detection rate of 75.9%, at a false-positive rate (FPR) of 4.9%. The predictive performance was improved by the addition of uterine artery Doppler PI and fetal crown-rump length (CRL) and with an estimated detection rate of 82.6%, at a FPR of 1.6%. Conclusion: A profound change in the first-trimester metabolite profile was noted in women who had subsequently developed early-onset PE. Preliminary algorithms appeared highly sensitive for first trimester prediction of early onset PE.