Untargeted metabolome quantitative trait locus mapping associates variation in urine glycerate to mutant glycerate kinase.

With successes of genome-wide association studies, molecular phenotyping systems are developed to identify genetically determined disease-associated biomarkers. Genetic studies of the human metabolome are emerging but exclusively apply targeted approaches, which restricts the analysis to a limited number of well-known metabolites. We have developed novel technical and statistical methods for systematic and automated quantification of untargeted NMR spectral data designed to perform robust and accurate quantitative trait locus (QTL) mapping of known and previously unreported molecular compounds of the metabolome. For each spectral peak, six summary statistics were calculated and independently tested for evidence of genetic linkage in a cohort of F2 (129S6xBALB/c) mice. The most significant evidence of linkages were obtained with NMR signals characterizing the glycerate (LOD10-42) at the mutant glycerate kinase locus, which demonstrate the power of metabolomics in quantitative genetics to identify the biological function of genetic variants. These results provide new insights into the resolution of the complex nature of metabolic regulations and novel analytical techniques that maximize the full utilization of metabolomic spectra in human genetics to discover mappable disease-associated biomarkers.

NMR-based metabolic profiling identifies biomarkers of liver regeneration following partial hepatectomy in the rat.

Tissue injury and repair are often overlapping consequences of disease or toxic exposure, but are not often considered as distinct processes in molecular studies. To establish the systemic metabolic response to liver regeneration, the partial hepatectomy (PH) model has been studied in the rat by an integrated metabonomics strategy, utilizing (1)H NMR spectroscopy of urine, liver and serum. Male Sprague-Dawley rats were subjected to either surgical removal of approximately two-thirds of the liver, sham operated (SO) surgery, or no treatment (n = 10/group) and samples collected over a 7 day period. A number of urinary metabolic perturbations were observed in PH rats compared with SO and control animals, including elevated levels of taurine, hypotaurine, creatine, guanidinoacetic acid, betaine, dimethylglycine and bile acids. Serum betaine and creatine were also elevated after PH, while levels of triglyceride were reduced. In the liver, triglycerides, cholesterol, alanine and betaine were elevated after PH, while choline and its derivatives were reduced. Upon examining the dynamic pattern of urinary response (the 'metabolic trajectory'), several metabolites could be categorized into groups likely to reflect perturbations to different processes such as dietary intake or hepatic 1-carbon metabolism. Several of the urinary perturbations observed during the regenerative phase of the PH model have also been observed after exposure to liver toxins, indicating that hepatic regeneration may make a contribution to the systemic alterations in metabolism associated with hepatotoxicity. The observed changes in 1-carbon and lipid metabolism are consistent with the proposed role of these pathways in the activation of a regenerative response and provide further evidence regarding the utility of urinary NMR profiles in the detection of liver-specific pathology. Biofluid (1)H NMR-based metabolic profiling provides new insight into the role of metabolism of liver regeneration, and suggests putative biomarkers for the noninvasive monitoring of the regeneration process.

Cluster analysis statistical spectroscopy using nuclear magnetic resonance generated metabolic data sets from perturbed biological systems.

We present a new approach for analysis, information recovery, and display of biological (1)H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz (1)H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancreatitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpretability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics.