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.

Prions are believed to spontaneously convert from a native, monomeric highly helical form (called PrP(c)) to a largely β-sheet-rich, multimeric and insoluble aggregate (called PrP(sc)). Because of its large size and insolubility, biophysical characterization of PrP(sc) has been difficult, and there are several contradictory or incomplete models of the PrP(sc) structure. A β-sheet-rich, soluble intermediate, called PrP(β), exhibits many of the same features as PrP(sc) and can be generated using a combination of low pH and/or mild denaturing conditions. Studies of the PrP(c) to PrP(β) conversion process and of PrP(β) folding intermediates may provide insights into the structure of PrP(sc). Using a truncated, recombinant version of Syrian hamster PrP(β) (shPrP(90-232)), we used NMR spectroscopy, in combination with other biophysical techniques (circular dichroism, dynamic light scattering, electron microscopy, fluorescence spectroscopy, mass spectrometry, and proteinase K digestion), to characterize the pH-driven PrP(c) to PrP(β) conversion process in detail. Our results show that below pH 2.8 the protein oligomerizes and conversion to the β-rich structure is initiated. At pH 1.7 and above, the oligomeric protein can recover its native monomeric state through dialysis to pH 5.2. However, when conversion is completed at pH 1.0, the large oligomer "locks down" irreversibly into a stable, β-rich form. At pH values above 3.0, the protein is amenable to NMR investigation. Chemical shift perturbations, NOE, amide line width, and T(2) measurements implicate the putative "amylome motif" region, "NNQNNF" as the region most involved in the initial helix-to-β conversion phase. We also found that acid-induced PrP(β) oligomers could be converted to fibrils without the use of chaotropic denaturants. The latter finding represents one of the first examples wherein physiologically accessible conditions (i.e., only low pH) were used to achieve PrP conversion and fibril formation.

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The "fit-for-purpose" approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS-based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.