The human urine metabolome Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri ...
PloS one,
09/2013, Volume:
8, Issue:
9
Journal Article
Peer reviewed
Open access
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.
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.
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.
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.
ClinicalTrials.gov NCT02052804.
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.
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.
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.
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.).
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.
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.
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 β-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.
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).
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.
Prions are believed to spontaneously convert from a native, monomeric highly helical form (called PrPc) to a largely β-sheet-rich, multimeric and insoluble aggregate (called PrPsc). Because of its ...large size and insolubility, biophysical characterization of PrPsc has been difficult, and there are several contradictory or incomplete models of the PrPsc structure. A β-sheet-rich, soluble intermediate, called PrPβ, exhibits many of the same features as PrPsc and can be generated using a combination of low pH and/or mild denaturing conditions. Studies of the PrPc to PrPβ conversion process and of PrPβ folding intermediates may provide insights into the structure of PrPsc. Using a truncated, recombinant version of Syrian hamster PrPβ (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 PrPc to PrPβ 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 PrPβ 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.
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.
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