Bile Acids Conjugation in Human Bile Is Not Random: New Insights from 1H-NMR Spectroscopy at 800 MHz

Bile acids constitute a group of structurally closely related molecules and represent the most abundant constituents of human bile. Investigations of bile acids have garnered increased interest owing to their recently discovered additional biological functions including their role as signaling molecules that govern glucose, fat and energy metabolism. Recent NMR methodological developments have enabled single-step analysis of several highly abundant and common glycine- and taurine- conjugated bile acids, such as glycocholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurocholic acid, taurodeoxycholic acid, and taurochenodeoxycholic acid. Investigation of these conjugated bile acids in human bile employing high field (800 MHz) (1)H-NMR spectroscopy reveals that the ratios between two glycine-conjugated bile acids and their taurine counterparts correlate positively (R2 = 0.83-0.97; p = 0.001 x 10(-2)-0.006 x 10(-7)) as do the ratios between a glycine-conjugated bile acid and its taurine counterpart (R2 = 0.92-0.95; p = 0.004 x 10(-3)-0.002 x 10(-10)). Using such correlations, concentration of individual bile acids in each sample could be predicted in good agreement with the experimentally determined values. These insights into the pattern of bile acid conjugation in human bile between glycine and taurine promise useful clues to the mechanism of bile acids' biosynthesis, conjugation and enterohepatic circulation, and may improve our understanding of the role of individual conjugated bile acids in health and disease

Esophageal Cancer Metabolite Biomarkers Detected by LC-MS and NMR Methods

Background: Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret’s esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication. Methodology/Principal Findings: Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p,0.05) between EAC patients and healthy controls. A partial leastsquares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

[Image: see text] NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many “unwanted” or “undesirable” compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment

Brain-Targeted Proanthocyanidin Metabolites for Alzheimer's Disease Treatment

While polyphenolic compounds have many health benefits, the potential development of polyphenols for the prevention/treatment of neurological disorders is largely hindered by their complexity as well as limited knowledge regarding their bioavailability, metabolism and bioactivity, especially in the brain. We recently demonstrated that dietary supplementation with a specific grape-derived polyphenolic preparation (GP) significantly improves cognitive function in a mouse model of Alzheimer’s disease (AD). GP is comprised of the proanthocyanidin (PAC) catechin and epicatechin in monomeric (Mo), oligomeric, and polymeric (Po) forms. In this study we report that following oral administration of the independent GP forms, only Mo is able to improve cognitive function and only Mo metabolites can selectively reach and accumulate in the brain at a concentration of ~400 nM. Most importantly we report for the first time that a biosynthetic epicatechin metabolite, 3’-O-methyl-epicatechin-5-O-β-glucuronide (3’-O-Me-EC-Gluc), one of the PAC metabolites identified in the brain following Mo treatment, promotes basal synaptic transmission and long term potentiation at physiologically relevant concentrations in hippocampus slices through mechanisms associated with cAMP response element binding protein (CREB) signaling. Our studies suggest that select brain-targeted PAC metabolites benefit cognition by improving synaptic plasticity in the brain, and provide impetus to develop 3’-O-Me-EC-Gluc and other brain-targeted PAC metabolites to promote learning and memory in Alzheimer’s disease and other forms of dementia

Elevated circulating levels of succinate in human obesity are linked to specific gut microbiota

Gut microbiota-related metabolites are potential clinical biomarkers for cardiovascular disease (CVD). Circulating succinate, a metabolite produced by both microbiota and the host, is increased in hypertension, ischemic heart disease, and type 2 diabetes. We aimed to analyze systemic levels of succinate in obesity, a major risk factor for CVD, and its relationship with gut microbiome. We explored the association of circulating succinate with specific metagenomic signatures in cross-sectional and prospective cohorts of Caucasian Spanish subjects. Obesity was associated with elevated levels of circulating succinate concomitant with impaired glucose metabolism. This increase was associated with specific changes in gut microbiota related to succinate metabolism: a higher relative abundance of succinate-producing Prevotellaceae (P) and Veillonellaceae (V), and a lower relative abundance of succinate-consuming Odoribacteraceae (O) and Clostridaceae (C) in obese individuals, with the (P + V/O + C) ratio being a main determinant of plasma succinate. Weight loss intervention decreased (P + V/O + C) ratio coincident with the reduction in circulating succinate. In the spontaneous evolution after good dietary advice, alterations in circulating succinate levels were linked to specific metagenomic signatures associated with carbohydrate metabolism and energy production with independence of body weight change. Our data support the importance of microbe-microbe interactions for the metabolite signature of gut microbiome and uncover succinate as a potential microbiota-derived metabolite related to CVD risk

Structural analysis of synthetic peptide fragments from EmrE, a multidrug resistance protein, in a membrane-mimetic environment

EmrE, a multidrug resistance protein from Escherichia coli, renders the bacterium resistant to a variety of cytotoxic drugs by active translocation out of the cell. The 110-residue sequence of EmrE limits the number of structural possibilities that can be envisioned for this membrane protein. Four helix bundle models have been considered [Yerushalmi, H., Lebendiker, M., and Schuldiner, S. (1996) J. Biol. Chem. 271, 31044-31048]. The validity of EmrE structural models has been probed experimentally by investigations on overlapping peptides (ranging in length from 19 to 27 residues), derived from the sequence of EmrE. The choice of peptides was made to provide sequences of two complete, predicted transmembrane helices (peptides H1 and H3) and two helix-loop-helix motifs (peptides A and B). Peptide (B) also corresponds to a putative hairpin in a speculative β-barrel model, with the "Pro-Thr-Gly" segment forming a turn. Structure determination in SDS micelles using NMR indicates peptide H1 to be predominantly helical, with helix boundaries in the micellar environment corroborating predicted helical limits. Peptide A adopts a helix-loop-helix structure in SDS micelles, and peptide B was also largely helical in micellar environments. An analogue peptide, C, in which the central "Pro-Thr-Gly" was replaced by "DPro-Gly" displays local turn conformation at the DPro-Gly segment, but neither a continuous helical stretch nor β-hairpin formation was observed. This study implies that the constraints of membrane and micellar environments largely direct the structure of transmembrane peptides and proteins and study of judiciously selected peptide fragments can prove useful in the structural elucidation of membrane proteins

Whole Blood Metabolomics by ¹H NMR Spectroscopy Provides a New Opportunity To Evaluate Coenzymes and Antioxidants

Conventional human blood metabolomics employs serum or plasma and provides a wealth of metabolic information therein. However, this approach lacks the ability to measure and evaluate important metabolites such as coenzymes and antioxidants that are present at high concentrations in red blood cells. As an important alternative to serum/plasma metabolomics, we show here that a simple ¹H NMR experiment can simultaneously measure coenzymes and antioxidants in extracts of whole human blood, in addition to the nearly 70 metabolites that were shown to be quantitated in serum/plasma recently [Anal. Chem. 2015, 87, 706−715]. Coenzymes of redox reactions: oxidized/reduced nicotinamide adenine dinucleotide (NAD⁺ and NADH) and nicotinamide adenine dinucleotide phosphate (NADP⁺ and NADPH); coenzymes of energy including adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP); and antioxidants, the sum of oxidized and reduced glutathione (GSSG and GSH) can be measured with essentially no additional effort. A new method was developed for detecting many of these unstable species without affecting other blood/blood plasma metabolites. The identities of coenzymes and antioxidants in blood NMR spectra were established combining 1D/2D NMR techniques, chemical shift databases, pH measurements and, finally, spiking with authentic compounds. This is the first study to report identification of major coenzymes and antioxidants and quantify them, simultaneously, with the large pool of other metabolites in human blood using NMR spectroscopy. Considering that the levels of coenzymes and antioxidants represent a sensitive measure of cellular functions in health and numerous diseases, the NMR method presented here potentially opens a new chapter in the metabolomics of blood