Philip Liu

DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs

DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.

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.

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.

SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database

The Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca) is a comprehensive, colorful, fully searchable and highly interactive database for visualizing human metabolic, drug action, drug metabolism, physiological activity and metabolic disease pathways. SMPDB contains >600 pathways with nearly 75% of its pathways not found in any other database. All SMPDB pathway diagrams are extensively hyperlinked and include detailed information on the relevant tissues, organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Since its last release in 2010, SMPDB has undergone substantial upgrades and significant expansion. In particular, the total number of pathways in SMPDB has grown by >70%. Additionally, every previously entered pathway has been completely redrawn, standardized, corrected, updated and enhanced with additional molecular or cellular information. Many SMPDB pathways now include transporter proteins as well as much more physiological, tissue, target organ and reaction compartment data. Thanks to the development of a standardized pathway drawing tool (called PathWhiz) all SMPDB pathways are now much more easily drawn and far more rapidly updated. PathWhiz has also allowed all SMPDB pathways to be saved in a BioPAX format. Significant improvements to SMPDB’s visualization interface now make the browsing, selection, recoloring and zooming of pathways far easier and far more intuitive. Because of its utility and breadth of coverage, SMPDB is now integrated into several other databases including HMDB and DrugBank.

Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

IntroductionKetosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.ObjectivesThe objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.MethodsTargeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n = 20) and ketotic (n = 6) cows by DI/LC-MS/MS at −8 and −4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.ResultsSignificant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.ConclusionsResults of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.

Combining a PagP fusion protein system with nickel ion-catalyzed cleavage to produce intrinsically disordered proteins in E. coli.

Many proteins contain intrinsically disordered regions that are highly solvent-exposed and susceptible to post-translational modifications. Studying these protein segments is critical to understanding their physiologic regulation, but proteolytic degradation can make them difficult to express and purify. We have designed a new protein expression vector that fuses the target protein to the N-terminus of the integral membrane protein, PagP. The two proteins are connected by a short linker containing the sequence SRHW, previously shown to be optimal for nickel ion-catalyzed cleavage. The methodology is demonstrated for an intrinsically disordered segment of cardiac troponin I. cTnI[135-209]-SRHW-PagP-His6 fusion protein was overexpressed in Escherichia coli, accumulating in insoluble inclusion bodies. The protein was solubilized, purified using nickel affinity chromatography, and then cleaved with 0.5mM NiSO4 at pH 9.0 and 45 °C, all in 6M guanidine-HCl. Nickel ion-catalyzed peptide bond hydrolysis is an effective chemical cleavage technique under denaturing conditions that preclude the use of proteases. Moreover, nickel-catalyzed cleavage is more specific than the most commonly used agent, cyanogen bromide, which cleaves C-terminal to methionine residues. We were able to produce 15 mg of purified cTnI[135-209] from 1L of M9 minimal media using this protocol. The methodology is more generally applicable to the production of intrinsically disordered protein segments.

Stereoselective Deuteration in Aspartate, Asparagine, Lysine, and Methionine Amino Acid Residues Using Fumarate as a Carbon Source for Escherichia coli in D2O

Perdeuteration with selective 1H,13C-enrichment of methyl groups has enabled solution NMR studies of large (>30 kDa) protein systems. However, we propose that for all non-methyl positions, only magnetization originating from 1H-12C groups is sufficiently long-lived, and it can be transferred via through-space NOEs to slowly relaxing 1H-15N or 1H-13C methyl groups to achieve multidimensional solution NMR. We demonstrate stereoselective 1H,12C-labeling by adding relatively inexpensive unlabeled carbon sources to Escherichia coli growth media in D2O. Using our model system, a mutant WW domain from human Pin1, we compare deuteration patterns in 19 amino acids (all except cysteine). Protein grown using glucose as the sole carbon source had high levels of protonation in aromatic rings and the Hβ positions of serine and tryptophan. In contrast, using our FROMP media (fumarate, rhamnose, oxalate, malonate, pyruvate), stereoselective protonation of Hβ2 with deuteration at Hα and Hβ3 was achieved in Asp, Asn, Lys, and Met residues. In solution NMR, stereospecific chemical shift assignments for Hβ are typically obtained in conjunction with χ1 dihedral angle determinations using 3-bond J-coupling (3JN-Hβ, 3JCO-Hβ, 3JHα-Hβ) experiments. However, due to motional averaging, the assumption of a pure rotameric state can yield incorrect χ1 dihedral angles with incorrect stereospecific assignments. This was the case for three residues in the Pin1 WW domain (Lys28, Met30, and Asn44). Thus, stereoselective 1H,12C-labeling will be useful not only for NMR studies of large protein systems, but also for determining side chain rotamers and dynamics in any protein system.