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.

Metabonomic investigations into the global biochemical sequelae of exposure to the pancreatic toxin 1-cyano-2-hydroxy-3-butene in the rat.

The time-related metabolic effects of 1-cyano-2-hydroxy-3-butene (CHB, crambene), a naturally occurring nitrile and experimental model toxin causing exocrine pancreatitis, have been investigated in rats using high-resolution NMR spectroscopy of urine and serum in combination with pattern recognition analysis. Rats were administered CHB subcutaneously in two doses, 15 mg/kg dose (n = 10) and 150 mg/kg (n = 10), and conventional histopathology and clinical chemistry assessments were performed. Urine samples were collected at - 16 and 0, 8, 24, 48, 72, 96, 120, 144 and 168 h postdosing and serum samples were collected at 48 and 168 h postdosing; these were analyzed using a range of 1D and 2D NMR spectroscopic methods. The metabolic profile perturbations seen throughout the time-course of the study are described, and the application of the spectral correlation technique Statistical TOtal Correlation SpectroscopY (STOCSY) to detect both structural and novel toxicological connectivities between xenobiotic and endogenous metabolite signals is illustrated for the first time. As a result, it is suggested that the STOCSY approach may be of wider application in the identification of toxic versus nontoxic metabolites in drug metabolism studies.

Temporal metabonomic modeling of l-arginine-induced exocrine pancreatitis.

The time-related metabolic responses to l-arginine (ARG)-induced exocrine pancreatic toxicity were investigated using single ip doses of 1,000 and 4,000 mg/kg body weight over a 7 day experimental period in male Sprague-Dawley rats. Sequential timed urine and plasma samples were analyzed using high resolution (1)H NMR spectroscopy together with complementary clinical chemistry and histopathology analyses. Principal components analysis (PCA) and orthogonal projection on latent structures discriminant analysis (O-PLS-DA) were utilized to analyze the (1)H NMR data and to extract and identify candidate biomarkers and to construct metabolic trajectories post ARG administration. Low doses of ARG resulted in virtually no histopathological damage and distinct reversible metabolic response trajectories. High doses of ARG caused pancreatic acinar degeneration and necrosis and characteristic metabolic trajectory profiles with several distinct phases. The initial trajectory phase (0-8 h) involved changes in the urea cycle and transamination indicating a homeostatic response to detoxify excess ammonia generated from ARG catabolism. By 48 h, there was a notable enhancement of the excretion of the gut microbial metabolites, phenylacetylglycine (PAG), 4-cresol-glucuronide and 4-cresol-sulfate, suggesting that compromised pancreatic function impacts on the activity of the gut microbiota giving potential rise to a novel class of surrogate extragenomic biomarkers of pancreatic injury. The implied compromise of microbiotal function may also contribute to secondary hepatic and pancreatic toxic responses. We show here for the first time the value of metabonomic studies in investigating metabolic disruption due to experimental pancreatitis. The variety of observed systemic responses suggests that this approach may be of general value in the assessment of other animal models or human pancreatitis.