High-Resolution Quantitative Metabolome Analysis of Urine by Automated Flow Injection NMR

Metabolism is essential to understand human health. To characterize human metabolism, a high-resolution read-out of the metabolic status under various physiological conditions, either in health or disease, is needed. Metabolomics offers an unprecedented approach for generating system-specific biochemical definitions of a human phenotype through the capture of a variety of metabolites in a single measurement. The emergence of large cohorts in clinical studies increases the demand of technologies able to analyze a large number of measurements, in an automated fashion, in the most robust way. NMR is an established metabolomics tool for obtaining metabolic phenotypes. Here, we describe the analysis of NMR-based urinary profiles for metabolic studies, challenged to a large human study (3007 samples). This method includes the acquisition of nuclear Overhauser effect spectroscopy one-dimensional and <i>J</i>-resolved two-dimensional (<i>J</i>-Res-2D) ¹H NMR spectra obtained on a 600 MHz spectrometer, equipped with a 120 μL flow probe, coupled to a flow-injection analysis system, in full automation under the control of a sampler manager. Samples were acquired at a throughput of ∼20 (or 40 when <i>J</i>-Res-2D is included) min/sample. The associated technical analysis error over the full series of analysis is 12%, which demonstrates the robustness of the method. With the aim to describe an overall metabolomics workflow, the quantification of 36 metabolites, mainly related to central carbon metabolism and gut microbial host cometabolism, was obtained, as well as multivariate data analysis of the full spectral profiles. The metabolic read-outs generated using our analytical workflow can therefore be considered for further pathway modeling and/or biological interpretation

Genome- and metabolome-wide analysis results, first stage.

<p>(A) Manhattan plot for feature 1.2025. (B) Genome- and metabolome-wide P-value heat map, showing associations with <i>P_C</i><5×10⁻⁸ in <i>CoLaus</i>. (C) Pseudo-spectrum for SNP rs37369, obtained by plotting the association P-values between rs37369 and all metabolic features.</p

Metabomatching.

<p>Each subfigure compares the <i>CoLaus</i> pseudo-spectrum (bottom half) with the NMR spectrum (top half) of the most likely candidate for the associated metabolite. (A) rs37369 vs. 3-aminoisobutyrate. (B) rs2147896 in <i>PYROXD2</i> vs. trimethylamine (C) rs8101881 in <i>SLC7A9</i> vs. lysine (D) rs281408 in <i>FUT2</i> vs. fucose.</p

Allelic heterogeneity at the <i>AGXT2</i> locus.

<p>Abbreviations: <i>P_C, P_T</i> – P-values, <i>x_C, x_T</i> – multivariate effect sizes, <i>R²</i> – explained variance of full model, <i>R²_diff</i> – additional explained variance of full model compared to best single SNP association, <i>model P</i> – probability of observing same or equal <i>R²_diff</i> increase with the same stepwise model selection for 2,500 permuted phenotypes.</p

Local Manhattan plots.

<p>The Manhattan plots show combined −log(P-values) in the neighborhood of the most strongly associated SNP for (A) the <i>FUT2</i> with fucose association, and (B) the <i>SLC7A9</i> with lysine association.</p

Locus-metabolite associations.

<p>For every locus, the association results are listed for the strongest association, after meta-analysis, of a SNP in the locus with a feature (bold) of the metabolite. Abbreviations: Chr – chromosome, Position – chromosomal position in NCBI build 36, <i>x_C</i> – effect size in <i>CoLaus</i>, <i>x_T</i> – effect size in <i>TasteSensomics</i>, <i>x_m</i> – effect size after meta-analysis, <i>P_m</i> – P-value after meta-analysis.</p

Genotype-Metabotype-Phenotype associations.

<p>The two novel gene-metabolite associations of this study implicate SNPs that had previously been associated with disease-related phenotypes by the indicated publications: (A) Fucose–Crohn's disease–<i>FUT2</i> (rs492602), (B) Lysine–eGFR–<i>SLC7A9</i> (rs8101881). A link between the metabolite and the phenotype has been established for (A) based on a mouse model and for (B) by a direct correlation with the indicated significance and effect size. Abbreviations: OR refers to the odds ratio, <i>x</i> to the linear regression effect size, <i>P</i> to the corresponding P-value, and the <i>m</i>-index indicates values obtained in the combined <i>CoLaus</i> and <i>TasteSensomics</i> sample.</p