Targeted Metabolic Profiling of Post-Mortem Brain from Infants Who Died from Sudden Infant Death Syndrome.

Currently little is known about the underlying pathophysiology associated with SIDS, and no objective biomarkers exist for the accurate identification of those at greatest risk of dying from SIDS. Using targeted metabolomics, we aim to profile the medulla oblongata of infants who have died from SIDS (n = 16) and directly compare their biochemical profile with age matched controls. Combining data acquired using 1H NMR and targeted DI-LC-MS/MS, we have identified fatty acid oxidation as a pivotal biochemical pathway perturbed in the brains of those infants who have from SIDS (p = 0.0016). Further we have identified a potential central biomarker with an AUC (95% CI) = 0.933 (0.845-1.000) having high sensitivity (0.933) and specificity (0.875) values for discriminating between control and SIDS brains. This is the first reported study to use targeted metabolomics for the study of PM brain from infants who have died from SIDS. We have identified pathways associated with the disease and central biomarkers for early screening/diagnosis.

Metabolic signatures of Huntington's disease (HD): (1)H NMR analysis of the polar metabolome in post-mortem human brain.

Huntington's disease (HD) is an autosomal neurodegenerative disorder affecting approximately 5-10 persons per 100,000 worldwide. The pathophysiology of HD is not fully understood but the age of onset is known to be highly dependent on the number of CAG triplet repeats in the huntingtin gene. Using (1)H NMR spectroscopy this study biochemically profiled 39 brain metabolites in post-mortem striatum (n=14) and frontal lobe (n=14) from HD sufferers and controls (n=28). Striatum metabolites were more perturbed with 15 significantly affected in HD cases, compared with only 4 in frontal lobe (p<0.05; q<0.3). The metabolite which changed most overall was urea which decreased 3.25-fold in striatum (p<0.01). Four metabolites were consistently affected in both brain regions. These included the neurotransmitter precursors tyrosine and l-phenylalanine which were significantly depleted by 1.55-1.58-fold and 1.48-1.54-fold in striatum and frontal lobe, respectively (p=0.02-0.03). They also included l-leucine which was reduced 1.54-1.69-fold (p=0.04-0.09) and myo-inositol which was increased 1.26-1.37-fold (p<0.01). Logistic regression analyses performed with MetaboAnalyst demonstrated that data obtained from striatum produced models which were profoundly more sensitive and specific than those produced from frontal lobe. The brain metabolite changes uncovered in this first (1)H NMR investigation of human HD offer new insights into the disease pathophysiology. Further investigations of striatal metabolite disturbances are clearly warranted.

Metabolomic prediction of endometrial cancer.

INTRODUCTION: Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality. OBJECTIVE: To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC. METHODS: We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I-II) and 10 late-stage (FIGO stages III-IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis. RESULTS: A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706-0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689-0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group. CONCLUSIONS: EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

The compound (3-{5-[(2,5-dimethoxyphenyl)amino]-1,3,4-thiadiazolidin-2-yl}-5,8-methoxy-2H-chromen-2-one) inhibits the prion protein conversion from PrPC to PrPSc with lower IC50 in ScN2a cells.

Prion diseases are fatal neurodegenerative disorders of the central nervous system characterized by the accumulation of a protease resistant form (PrPSc) of the cellular prion protein (PrPC) in the brain. Two types of cellular prion (PrPC) compounds have been identified that appear to affect prion conversion are known as Effective Binders (EBs) and Accelerators (ACCs). Effective binders shift the balance in favour of PrPC, whereas Accelerators favour the formation of PrPSc. Molecular docking indicates EBs and ACCs both bind to pocket-D of the SHaPrPC molecule. However, EBs and ACCs may have opposing effects on the stability of the salt bridge between Arg156 and Glu196/Glu200. Computational docking data indicate that the hydrophobic benzamide group of the EB, GFP23 and the 1-(3,3-dimethylcyclohexylidene)piperidinium group of the ACC, GFP22 play an important role in inhibition and conversion from SHaPrPC to SHaPrPSc, respectively. Experimentally, NMR confirmed the amide chemical shift perturbations observed upon the binding of GFP23 to pocket-D of SHaPrPC. Consistent with its role as an ACC, titration of GFP22 resulted in widespread chemical shift changes and signal intensity loss due to protein unfolding. Virtual screening of a ligand database using the molecular scaffold developed from the set of EBs identified six of our compounds (previously studied using fluorescence quenching) as being among the top 100 best binders. Among them, compounds 5 and 6 were found to be particularly potent in decreasing the accumulation SHaPrPSc in ScN2a cells with an IC50 of ∼35µM and 20µM.

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.).

Brassica villosa, a system for studying non-glandular trichomes and genes in the Brassicas.

Brassica villosa is a wild Brassica C genome species with very dense trichome coverage and strong resistance to many insect pests of Brassica oilseeds and vegetables. Transcriptome analysis of hairy B. villosa leaves indicated higher expression of several important trichome initiation genes compared with glabrous B. napus leaves and consistent with the Arabidopsis model of trichome development. However, transcripts of the TRY inhibitory gene in hairy B. villosa were surprisingly high relative to B. napus and relative transcript levels of SAD2, EGL3, and several XIX genes were low, suggesting potential ancillary or less important trichome-related roles for these genes in Brassica species compared with Arabidopsis. Several antioxidant, calcium, non-calcium metal and secondary metabolite genes also showed differential expression between these two species. These coincided with accumulation of two alkaloid-like compounds, high levels of calcium, and other metals in B. villosa trichomes that are correlated with the known tolerance of B. villosa to high salt and the calcium-rich natural habitat of this wild species. This first time report on the isolation of large amounts of pure B. villosa trichomes, on trichome content, and on relative gene expression differences in an exceptionally hairy Brassica species compared with a glabrous species opens doors for the scientific community to understand trichome gene function in the Brassicas and highlights the potential of B. villosa as a trichome research platform.

Metabolomic prediction of fetal congenital heart defect in the first trimester.

OBJECTIVE: The objective of the study was to identify metabolomic markers in maternal first-trimester serum for the detection of fetal congenital heart defects (CHDs). STUDY DESIGN: Mass spectrometry (direct injection/liquid chromatography and tandem mass spectrometry) and nuclear magnetic resonance spectrometry-based metabolomic analyses were performed between 11 weeks' and 13 weeks 6 days' gestation on maternal serum. A total of 27 CHD cases and 59 controls were compared. There were no known or suspected chromosomal or syndromic abnormalities indicated. RESULTS: A total of 174 metabolites were identified and quantified using the 2 analytical methods. There were 14 overlapping metabolites between platforms. We identified 123 metabolites that demonstrated significant differences on a univariate analysis in maternal first-trimester serum in CHD vs normal cases. There was a significant disturbance in acylcarnitine, sphingomyelin, and other metabolite levels in CHD pregnancies. Predictive algorithms were developed for CHD detection. High sensitivity (0.929; 95% confidence interval [CI], 0.92-1.00) and specificity (0.932; 95% CI, 0.78-1.00) for CHD detection were achieved (area under the curve, 0.992; 95% CI, 0.973-1.0). CONCLUSION: In the first such report, we demonstrated the feasibility of the use of metabolomic developing biomarkers for the first-trimester prediction of CHD. Abnormal lipid metabolism appeared to be a significant feature of CHD pregnancies.

Relative and regional stabilities of the hamster, mouse, rabbit, and bovine prion proteins toward urea unfolding assessed by nuclear magnetic resonance and circular dichroism spectroscopies.

The residue-specific urea-induced unfolding patterns of recombinant prion proteins from different species (bovine, rabbit, mouse, and Syrian hamster) were monitored using high-resolution (1)H nuclear magnetic resonance (NMR) spectroscopy. Protein constructs of different lengths, and with and without a His tag attached at the N-terminus, were studied. The various species showed different overall sensitivities toward urea denaturation with stabilities in the following order: hamster ≤ mouse < rabbit < bovine protein. This order is in agreement with recent circular dichroism (CD) spectroscopic measurements for several species [Khan, M. Q. (2010) Proc. Natl. Acad. Sci. U.S.A.107, 19808-19813] and for the bovine protein presented herein. The [urea](1/2) values determined by CD spectroscopy parallel those of the most stable residues observed by NMR spectroscopy. Neither the longer constructs containing an additional hydrophobic region nor the His tag influenced the stability of the structured domain of the constructs studied. The effect of the S174N mutation in rabbit PrP(C) was also investigated. The rank order of the regional stabilities within each protein remained the same for all species. In particular, the residues in the ß-sheet region in all four species were more sensitive to urea-induced unfolding than residues in the α2 and α3 helical regions. These observations indicate that the regional specific unfolding pattern is the same for the four mammalian prion proteins studied but militate against the idea that PrP(Sc) formation is linked with the global stability of PrP(C).

Detailed biophysical characterization of the acid-induced PrP(c) to PrP(ß) conversion process.

Prions are believed to spontaneously convert from a native, monomeric highly helical form (called PrP(c)) to a largely ß-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 ß-sheet-rich, soluble intermediate, called PrP(ß), 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(ß) conversion process and of PrP(ß) folding intermediates may provide insights into the structure of PrP(sc). Using a truncated, recombinant version of Syrian hamster PrP(ß) (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(ß) conversion process in detail. Our results show that below pH 2.8 the protein oligomerizes and conversion to the ß-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, ß-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-ß conversion phase. We also found that acid-induced PrP(ß) 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.

First-trimester metabolomic prediction of stillbirth.

Background: Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority. Objective: To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach. Methods: Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (<28 weeks and <32 weeks), those related to growth restriction/placental disorder, and unexplained stillbirths were evaluated. Results: Targeted metabolites including glycine, acetic acid, L-carnitine, creatine, lysoPCaC18:1, PCaeC34:3, and PCaeC44:4 predicted stillbirth overall with an area under the curve [AUC, 95% confidence interval (CI)] = 0.707 (0.628-0.785). When combined with clinical predictors the AUC value increased to 0.740 (0.667-0.812). First-trimester targeted metabolites also significantly predicted early, unexplained, and placental-related stillbirths. Untargeted LC-MS features combined with other clinical predictors achieved an AUC (95%CI) = 0.860 (0.793-0.927) for the prediction of stillbirths overall. We found novel preliminary evidence that, verruculotoxin, a toxin produced by common household molds, might be linked to stillbirth. Conclusions: We have identified novel biomarkers for stillbirth using metabolomics and demonstrated the feasibility of first-trimester prediction.

MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids.

BACKGROUND: One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies. RESULTS: We describe a standalone graphics software tool, called MetaboMiner, which can be used to automatically or semi-automatically identify metabolites in complex biofluids from 2D NMR spectra. MetaboMiner is able to handle both 1H-1H total correlation spectroscopy (TOCSY) and 1H-13C heteronuclear single quantum correlation (HSQC) data. It identifies compounds by comparing 2D spectral patterns in the NMR spectrum of the biofluid mixture with specially constructed libraries containing reference spectra of approximately 500 pure compounds. Tests using a variety of synthetic and real spectra of compound mixtures showed that MetaboMiner is able to identify >80% of detectable metabolites from good quality NMR spectra. CONCLUSION: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra. Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.

A simple in vitro assay for assessing the efficacy, mechanisms and kinetics of anti-prion fibril compounds.

Prion diseases are caused by the conversion of normal cellular prion proteins (PrP) into lethal prion aggregates. These prion aggregates are composed of proteinase K (PK) resistant fibrils and comparatively PK-sensitive oligomers. Currently there are no anti-prion pharmaceuticals available to treat or prevent prion disease. Methods of discovering anti-prion molecules rely primarily on relatively complex cell-based, tissue slice or animal-model assays that measure the effects of small molecules on the formation of PK-resistant prion fibrils. These assays are difficult to perform and do not detect the compounds that directly inhibit oligomer formation or alter prion conversion kinetics. We have developed a simple cell-free method to characterize the impact of anti-prion fibril compounds on both the oligomer and fibril formation. In particular, this assay uses shaking-induced conversion (ShIC) of recombinant PrP in a 96-well format and resolution enhanced native acidic gel electrophoresis (RENAGE) to generate, assess and detect PrP fibrils in a high throughput fashion. The end-point PrP fibrils from this assay can be further characterized by PK analysis and negative stain transmission electron microscopy (TEM). This cell-free, gel-based assay generates metrics to assess anti-prion fibril efficacy and kinetics. To demonstrate its utility, we characterized the action of seven well-known anti-prion molecules: Congo red, curcumin, GN8, quinacrine, chloropromazine, tetracycline, and TUDCA (taurourspdeoxycholic acid), as well as four suspected anti-prion compounds: trans-resveratrol, rosmarinic acid, myricetin and ferulic acid. These findings suggest that this in vitro assay could be useful in identifying and comprehensively assessing novel anti-prion fibril compounds. Abbreviations: PrP, prion protein; PK, proteinase K; ShIC, shaking-induced conversion; RENAGE, resolution enhanced native acidic gel electrophoresis; TEM, transmission electron microscopy; TUDCA, taurourspdeoxycholic acid; BSE, bovine spongiform encephalopathy; CWD, chronic wasting disease; CJD, Creutzfeldt Jakob disease; GSS, Gerstmann-Sträussler-Scheinker syndrome; FFI, fatal familial insomnia; PrPc, cellular prion protein; recPrPC, recombinant monomeric prion protein; PrPSc, infectious particle of misfolded prion protein; RT-QuIC, real-time quaking-induced conversion; PMCA, Protein Misfolding Cyclic Amplification; LPS, lipopolysaccharide; EGCG, epigallocatechin gallate; GN8, 2-pyrrolidin-1-yl-N-[4-[4-(2-pyrrolidin-1-yl-acetylamino)-benzyl]-phenyl]-acetamide; DMSO, dimethyl sulfoxide; ScN2A, scrapie infected neuroblastoma cells; IC50, inhibitory concentration for 50% reduction; recMoPrP 23-231, recombinant full-length mouse prion protein residues 23-231; EDTA; PICUP, photo-induced cross-linking of unmodified protein; BSA, bovine serum albumin;; PMSF, phenylmethanesulfonyl fluoride.

Residue-specific mobility changes in soluble oligomers of the prion protein define regions involved in aggregation.

Prion (PrP) diseases are neurodegenerative diseases characterized by the formation of ß-sheet rich, insoluble and protease resistant protein deposits (called PrPSc) that occur throughout the brain. Formation of synthetic or in vitro PrPSc can occur through on-pathway toxic oligomers. Similarly, toxic and infectious oligomers identified in cell and animal models of prion disease indicate that soluble oligomers are likely intermediates in the formation of insoluble PrPSc. Despite the critical role of prion oligomers in disease progression, little is known about their structure. In order, to obtain structural insight into prion oligomers, we generated oligomers by shaking-induced conversion of recombinant, monomeric prion protein PrPc (spanning residues 90-231). We then obtained two-dimensional solution NMR spectra of the PrPc monomer, a 40% converted oligomer, and a 94% converted oligomer. Heteronuclear single-quantum correlation (1H-15N) studies revealed that, in comparison to monomeric PrPc, the oligomer has intense amide peak signals in the N-terminal (residues 90-114) and C-terminal regions (residues 226-231). Furthermore, a core region with decreased mobility is revealed from residues ~127 to 225. Within this core oligomer region with decreased mobility, there is a pocket of increased amide peak signal corresponding to the middle of α-helix 2 and the loop between α-helices 2 and 3 in the PrPc monomer structure. Using high-resolution solution-state NMR, this work reveals detailed and divergent residue-specific changes in soluble oligomeric models of PrP.

Microbiome and metabolome modifying effects of several cardiovascular disease interventions in apo-E-/- mice.

BACKGROUND: There is strong evidence indicating that gut microbiota have the potential to modify, or be modified by the drugs and nutritional interventions that we rely upon. This study aims to characterize the compositional and functional effects of several nutritional, neutraceutical, and pharmaceutical cardiovascular disease interventions on the gut microbiome, through metagenomic and metabolomic approaches. Apolipoprotein-E-deficient mice were fed for 24 weeks either high-fat/cholesterol diet alone (control, HFC) or high-fat/cholesterol in conjunction with one of three dietary interventions, as follows: plant sterol ester (PSE), oat ß-glucan (OBG) and bile salt hydrolase-active Lactobacillus reuteri APC 2587 (BSH), or the drug atorvastatin (STAT). The gut microbiome composition was then investigated, in addition to the host fecal and serum metabolome. RESULTS: We observed major shifts in the composition of the gut microbiome of PSE mice, while OBG and BSH mice displayed more modest fluctuations, and STAT showed relatively few alterations. Interestingly, these compositional effects imparted by PSE were coupled with an increase in acetate and reduction in isovalerate (p < 0.05), while OBG promoted n-butyrate synthesis (p < 0.01). In addition, PSE significantly dampened the microbial production of the proatherogenic precursor compound, trimethylamine (p < 0.05), attenuated cholesterol accumulation, and nearly abolished atherogenesis in the model (p < 0.05). However, PSE supplementation produced the heaviest mice with the greatest degree of adiposity (p < 0.05). Finally, PSE, OBG, and STAT all appeared to have considerable impact on the host serum metabolome, including alterations in several acylcarnitines previously associated with a state of metabolic dysfunction (p < 0.05). CONCLUSIONS: We observed functional alterations in microbial and host-derived metabolites, which may have important implications for systemic metabolic health, suggesting that cardiovascular disease interventions may have a significant impact on the microbiome composition and functionality. This study indicates that the gut microbiome-modifying effects of novel therapeutics should be considered, in addition to the direct host effects.

Metabolomic fingerprint of heart failure with preserved ejection fraction.

BACKGROUND: Heart failure (HF) with preserved ejection fraction (HFpEF) is increasingly recognized as an important clinical entity. Preclinical studies have shown differences in the pathophysiology between HFpEF and HF with reduced ejection fraction (HFrEF). Therefore, we hypothesized that a systematic metabolomic analysis would reveal a novel metabolomic fingerprint of HFpEF that will help understand its pathophysiology and assist in establishing new biomarkers for its diagnosis. METHODS AND RESULTS: Ambulatory patients with clinical diagnosis of HFpEF (n = 24), HFrEF (n = 20), and age-matched non-HF controls (n = 38) were selected for metabolomic analysis as part of the Alberta HEART (Heart Failure Etiology and Analysis Research Team) project. 181 serum metabolites were quantified by LC-MS/MS and 1H-NMR spectroscopy. Compared to non-HF control, HFpEF patients demonstrated higher serum concentrations of acylcarnitines, carnitine, creatinine, betaine, and amino acids; and lower levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins. Medium and long-chain acylcarnitines and ketone bodies were higher in HFpEF than HFrEF patients. Using logistic regression, two panels of metabolites were identified that can separate HFpEF patients from both non-HF controls and HFrEF patients with area under the receiver operating characteristic (ROC) curves of 0.942 and 0.981, respectively. CONCLUSIONS: The metabolomics approach employed in this study identified a unique metabolomic fingerprint of HFpEF that is distinct from that of HFrEF. This metabolomic fingerprint has been utilized to identify two novel panels of metabolites that can separate HFpEF patients from both non-HF controls and HFrEF patients. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT02052804.

Accurate, fully-automated NMR spectral profiling for metabolomics.

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Lipopolysaccharide induced conversion of recombinant prion protein.

The conformational conversion of the cellular prion protein (PrP(C)) to the ß-rich infectious isoform PrP(Sc) is considered a critical and central feature in prion pathology. Although PrP(Sc) is the critical component of the infectious agent, as proposed in the "protein-only" prion hypothesis, cellular components have been identified as important cofactors in triggering and enhancing the conversion of PrP(C) to proteinase K resistant PrP(Sc). A number of in vitro systems using various chemical and/or physical agents such as guanidine hydrochloride, urea, SDS, high temperature, and low pH, have been developed that cause PrP(C) conversion, their amplification, and amyloid fibril formation often under non-physiological conditions. In our ongoing efforts to look for endogenous and exogenous chemical mediators that might initiate, influence, or result in the natural conversion of PrP(C) to PrP(Sc), we discovered that lipopolysaccharide (LPS), a component of gram-negative bacterial membranes interacts with recombinant prion proteins and induces conversion to an isoform richer in ß sheet at near physiological conditions as long as the LPS concentration remains above the critical micelle concentration (CMC). More significant was the LPS mediated conversion that was observed even at sub-molar ratios of LPS to recombinant ShPrP (90-232).

Molecular docking of thiamine reveals similarity in binding properties between the prion protein and other thiamine-binding proteins.

Prion-induced diseases are a global health concern. The lack of effective therapy and 100% mortality rates for such diseases have made the prion protein an important target for drug discovery. Previous NMR experimental work revealed that thiamine and its derivatives bind the prion protein in a pocket near the N-terminal loop of helix 1, and conserved intermolecular interactions were noted between thiamine and other thiamine-binding proteins. Furthermore, water-mediated interactions were observed in all of the X-ray crystallographic structures of thiamine-binding proteins, but were not observed in the thiamine-prion NMR study. To better understand the potential role of water in thiamine-prion binding, a docking study was employed using structural X-ray solvent. Before energy minimization, docked thiamine assumed a "V" shape similar to some of the known thiamine-dependent proteins. Following minimization with NMR-derived restraints, the "F" conformation was observed. Our findings confirmed that water is involved in ligand stabilization and phosphate group interaction. The resulting refined structure of thiamine bound to the prion protein allowed the 4-aminopyrimidine ring of thiamine to π-stack with Tyr150, and facilitated hydrogen bonding between Asp147 and the amino group of 4-aminopyrimidine. Investigation of the π-stacking interaction through mutation of the tyrosine residue further revealed its importance in ligand placement. The resulting refined structure is in good agreement with previous experimental restraints, and is consistent with the pharmacophore model of thiamine-binding proteins.

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.