The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB’s search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB’s ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

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Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID’s performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID’s compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID’s performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID’s compound identification abilities; (3) the development of new scoring functions that improves CFM-ID’s accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.

YMDB or the Yeast Metabolome Database (http://www.ymdb.ca/) is a comprehensive database containing extensive information on the genome and metabolome of Saccharomyces cerevisiae. Initially released in 2012, the YMDB has gone through a significant expansion and a number of improvements over the past 4 years. This manuscript describes the most recent version of YMDB (YMDB 2.0). More specifically, it provides an updated description of the database that was previously described in the 2012 NAR Database Issue and it details many of the additions and improvements made to the YMDB over that time. Some of the most important changes include a 7-fold increase in the number of compounds in the database (from 2007 to 16 042), a 430-fold increase in the number of metabolic and signaling pathway diagrams (from 66 to 28 734), a 16-fold increase in the number of compounds linked to pathways (from 742 to 12 733), a 17-fold increase in the numbers of compounds with nuclear magnetic resonance or MS spectra (from 783 to 13 173) and an increase in both the number of data fields and the number of links to external databases. In addition to these database expansions, a number of improvements to YMDB's web interface and its data visualization tools have been made. These additions and improvements should greatly improve the ease, the speed and the quantity of data that can be extracted, searched or viewed within YMDB. Overall, we believe these improvements should not only improve the understanding of the metabolism of S. cerevisiae, but also allow more in-depth exploration of its extensive metabolic networks, signaling pathways and biochemistry.

Roots of the herb Panax ginseng are known to contain high levels of bioactive saponins. Here, we isolated saponins from ginseng root powder and studied their inhibitory effect on the absorption of dietary fat in male Balb/c mice. Consumption of ginseng saponins suppressed the expected increase in body weight and plasma triacylglycerols, following a high-fat diet and observed higher intake. Consumption of ginseng saponins had no effect on the concentration of the total plasma cholesterol in both chow and high-fat diets in mice. The mode by which saponins from ginseng inhibit lipid metabolism was assessed as the in vitro inhibition of pancreatic lipase. Ginseng saponin inhibited pancreatic lipase with an apparent IC50 value of 500 mug/mL. Our results suggest that the anti-obesity and hypolipidemic effects of Ginseng in high-fat diet-treated mice were attributed to the isolated saponin fraction. These metabolic effects of the ginseng saponins may be mediated by inhibition of pancreatic lipase activity.

BackgroundAdults with chronic kidney disease (CKD) exhibit alterations in tryptophan metabolism, mainly via the kynurenine pathway, due to higher enzymatic activity induced mainly by inflammation. Indoles produced by gut-microflora are another group of tryptophan metabolites related to inflammation and conditions accompanying CKD. Disruptions in tryptophan metabolism have been associated with various neurological and psychological disorders. A high proportion of CKD patients self-report symptoms of depression and/or anxiety and decline in cognitive functioning. This pilot study examines tryptophan metabolism in CKD and explores associations with psychological and cognitive functioning.MethodsTwenty-seven adults with CKD were part of 49 patients recruited to participate in a prospective pilot study, initially with an eGFR of 15–29 mL/min/1.73 m2. Only participants with viable blood samples and complete psychological/cognitive data at a 2-year follow-up were included in the reported cross-sectional study. Serum samples were analysed by Liquid Chromatography coupled to Mass Spectrometry, for tryptophan, ten of its metabolites, the inflammation marker neopterin and the hypothalamic–pituitary–adrenal (HPA) axis marker cortisol.ResultsThe tryptophan breakdown index (kynurenine / tryptophan) correlated with neopterin (Pearson R = 0.51 P = 0.006) but not with cortisol. Neopterin levels also correlated with indoxyl sulfate (R = 0.68, P < 0.0001) and 5 metabolites of tryptophan (R range 0.5–0.7, all P ≤ 0.01), which were all negatively related to eGFR (P < 0.05). Higher levels of kynurenic acid were associated with lower cognitive functioning (Spearman R = −0.39, P < 0.05), while indole-3 acetic acid (IAA) was correlated with anxiety and depression (R = 0.52 and P = 0.005, R = 0.39 and P < 0.05, respectively).ConclusionsThe results of this preliminary study suggest the involvement of inflammation in tryptophan breakdown via the kynurenine pathway, yet without sparing tryptophan metabolism through the 5-HT (serotonin) pathway in CKD patients. The multiple moderate associations between indole-3 acetic acid and psychological measures were a novel finding. The presented pilot data necessitate further exploration of these associations within a large prospective cohort to assess the broader significance of these findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12882-016-0387-3) contains supplementary material, which is available to authorized users.

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