We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11

A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

Coupling evolution periods in NMR experiments on proteins has recently attracted much attention for its substantial savings in measurement time. Using the concept of multiway decomposition, which already proved useful in many types of NMR applications, the novel tool PRODECOMP decomposes sets of spectra with coupled evolution periods and attempts to provide all information that would be found in a corresponding full-dimensional spectrum. It offers the following advantages: (1) all experimental spectra are used simultaneously, avoiding sensitivity loss associated with individual examination of the spectra, (2) folding (aliasing) caused by the linear combinations of individual shifts in the projected spectra is automatically resolved, allowing for better resolution due to smaller spectral widths, (3) various types of schemes for the coupling of evolution periods are possible, (4) reconstructions of various high-dimensional spectra, including the corresponding full-dimensional spectrum, are straightforward, (5) overlap in both the projected and the directly detected dimensions is efficiently resolved. The capabilities of PRODECOMP are illustrated for a 14 kD protein, for which 12 projections of a five-dimensional spectrum with the nuclei N, HN, CO, Calpha, and Halpha are analyzed.

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.

Aim Determine the metabolic profile and identify risk factors of women transitioning from gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM). Methods 237 women diagnosed with GDM underwent an oral glucose tolerance test (OGTT), anthropometrics assessment, and completed lifestyle questionnaires six years after pregnancy. Blood was analysed for clinical variables (e.g., insulin, glucose, HbA1c, adiponectin, leptin, and lipid levels) and NMR metabolomics. Based on the OGTT, women were divided into three groups: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and T2DM. Results Six years after GDM, 19% of subjects had T2DM and 19% IGT. After BMI adjustment, the IGT group had lower HDL, higher leptin, and higher free fatty acid (FFA) levels, and the T2DM group higher triglyceride, FFA, and C-reactive protein levels than the NGT group. IGT and T2DM groups reported lower physical activity. NMR measurements revealed that levels of branched-chain amino acids (BCAAs) and the valine metabolite 3-hydroxyisobyturate were higher in T2DM and IGT groups and correlated with measures of insulin resistance and lipid metabolism. Conclusion In addition to well-known clinical risk factors, BCAAs and 3-hydroxyisobyturate are potential markers to be evaluated as predictors of metabolic risk after pregnancy complicated by GDM.

BackgroundObjective and reliable methods to measure dietary exposure and prove associations and causation between diet and health are desirable.ObjectiveThe aim of this study was to investigate if 1H-nuclear magnetic resonance (1H-NMR) analysis of serum samples may be used as an objective method to discriminate vegan, vegetarian, and omnivore diets. Specifically, the aim was to identify a metabolite pattern that separated meat-eaters from non–meat-eaters and vegans from nonvegans.MethodsHealthy volunteers (45 men and 75 women) complying with habitual vegan (n = 43), vegetarian (n = 24 + vegetarians adding fish n = 13), or omnivore (n = 40) diets were enrolled in the study. Data were collected on clinical phenotype, body composition, lifestyle including a food-frequency questionnaire (FFQ), and a 4-d weighed food diary. Serum samples were analyzed by routine clinical test and for metabolites by 1H-NMR spectroscopy. NMR data were nonnormalized, UV-scaled, and analyzed with multivariate data analysis [principal component analysis, orthogonal projections to latent structures (OPLS) and OPLS with discriminant analysis]. In the multivariate analysis volunteers were assigned as meat-eaters (omnivores), non–meat-eaters (vegans and vegetarians), vegans, or nonvegans (lacto-ovo-vegetarians, vegetarians adding fish, and omnivores). Metabolites were identified by line-fitting of 1D 1H-NMR spectra and the use of statistical total correlation spectroscopy.ResultsAlthough many metabolites differ in concentration between men and women as well as by age, body mass index, and body composition, it was possible to correctly classify 97.5% of the meat-eaters compared with non–meat-eaters and 92.5% of the vegans compared with nonvegans. The branched-chain amino acids, creatine, lysine, 2-aminobutyrate, glutamine, glycine, trimethylamine, and 1 unidentified metabolite were among the most important metabolites in the discriminating patterns in relation to intake of both meat and other animal products.Conclusions 1H-NMR serum metabolomics appears to be a possible objective tool to identify and predict habitual intake of meat and other animal products in healthy subjects. These results should be confirmed in larger cohort studies or intervention trials. This trial was registered at clinicaltrials.gov as NCT02039609.

Full automation of the analysis of spectra is a prerequisite for high-throughput NMR studies in structural or functional genomics. Sequence-specific assignments often form the major bottleneck. Here, we present a procedure that yields nearly complete backbone and side chain resonance assignments starting from a set of heteronuclear three-dimensional spectra. Neither manual intervention, e.g., to correct lists obtained from peak picking before feeding these to an assignment program, nor protein-specific information, e.g., structures of homologous proteins, were required. By combining two earlier published procedures, AUTOPSY [Koradi et al. (1998) J. Magn. Reson., 135, 288-297] and GARANT [Bartels et al. (1996) J. Biomol. NMR, 7, 207-213], with a new program, PICS, all necessary steps from spectra analyses to sequence-specific assignments were performed fully automatically. Characteristic features of the present approach are a flexible design allowing as input almost any combination of NMR spectra, applicability to side chains, robustness with respect to parameter choices (such as noise levels) and reproducibility. In this study, automated resonance assignments were obtained for the 14 kD blue copper protein azurin from P. aeruginosa using five spectra: HNCACB, HNHA, HCCH-TOCSY, 15N-NOESY-HSQC and 13C-NOESY-HSQC. Peaks from these three-dimensional spectra were filtered and calibrated with the help of two two-dimensional spectra: 15N-HSQC and 13C-HSQC. The rate of incorrect assignments is less than 1.5% for backbone nuclei and about 3.5% when side chain protons are also considered.

MotivationBiobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.ResultsThis study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1–36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.Availability and implementationThe sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift.Supplementary information Supplementary data are available at Bioinformatics online.