Trent C. Bjorndahl

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.

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

BackgroundOne-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.ResultsWe 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 ~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.ConclusionMetaboMiner 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.

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

The prion protein binds thiamine

Although highly conserved throughout evolution, the exact biological function of the prion protein is still unclear. In an effort to identify the potential biological functions of the prion protein we conducted a small‐molecule screening assay using the Syrian hamster prion protein [shPrP(90–232)]. The screen was performed using a library of 149 water‐soluble metabolites that are known to pass through the blood–brain barrier. Using a combination of 1D NMR, fluorescence quenching and surface plasmon resonance we identified thiamine (vitamin B1) as a specific prion ligand with a binding constant of ∼ 60 μm. Subsequent studies showed that this interaction is evolutionarily conserved, with similar binding constants being seen for mouse, hamster and human prions. Various protein construct lengths, both with and without the unstructured N‐terminal region in the presence and absence of copper, were examined. This indicates that the N‐terminus has no influence on the protein’s ability to interact with thiamine. In addition to thiamine, the more biologically abundant forms of vitamin B1 (thiamine monophosphate and thiamine diphosphate) were also found to bind the prion protein with similar affinity. Heteronuclear NMR experiments were used to determine thiamine’s interaction site, which is located between helix 1 and the preceding loop. These data, in conjunction with computer‐aided docking and molecular dynamics, were used to model the thiamine‐binding pharmacophore and a comparison with other thiamine binding proteins was performed to reveal the common features of interaction.

Lipopolysaccharide induced conversion of recombinant prion protein

The conformational conversion of the cellular prion protein (PrPC) to the β-rich infectious isoform PrPSc is considered a critical and central feature in prion pathology. Although PrPSc 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 PrPC to proteinase K resistant PrPSc. 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 PrPC 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 PrPC to PrPSc, 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 β 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).

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

Huntingtons 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.

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.

Correction: Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics.

The third author’s name is spelled incorrectly. The correct name is: Trent C. Bjorndahl. The correct citation is: Ravanbakhsh S, Liu P, Bjorndahl TC, Mandal R, Grant JR, Wilson M, et al. (2015) Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics. PLoS ONE 10(5): e0124219. doi:10.1371/journal.pone.0124219

Metabolomic prediction of endometrial cancer

IntroductionEndometrial 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.ObjectiveTo determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.MethodsWe 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.ResultsA 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.ConclusionsEC 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.

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.