Multi-platform investigation of the metabolome in a leptin receptor defective murine model of type 2 diabetes.

We describe a multi-platform ((1)H NMR, LC-MS, microarray) investigation of metabolic disturbances associated with the leptin receptor defective (db/db) mouse model of type 2 diabetes using novel assignment methodologies. For the first time, several urinary metabolites were found to be associated with diabetes and/or diabetes progression and confirmed in both NMR and LC-MS datasets. The confirmed metabolites were trimethylamine-n-oxide (TMAO), creatine, carnitine, and phenylalanine. TMAO and phenylalanine were both elevated in db/db mice and decreased in these mice with age. Levels of both creatine and carnitine increase in diabetic mice with age and creatine was also significantly decreased in db/db mice. Additionally, many metabolic markers were found by either NMR or LC-MS, but could not be found in both, due to instrumental limitations. This indicates that the combined use of NMR and LC-MS instrumentation provides complementary information that would be otherwise unattainable. Pathway analyses of urinary metabolites and liver, muscle, and adipose tissue transcripts from the db/db model were also performed to identify altered biochemical processes in the diabetic mice. Metabolite and liver transcript levels associated with the TCA cycle and steroid processes were altered in db/db mice. In addition, gene expression in muscle and liver associated with fatty acid processing was altered in the diabetic mice and similar evidence was observed in the LC-MS data. Our findings highlight the importance of a number of processes known to be associated with diabetes and reveal tissue specific responses to the condition. When studying metabolic disorders such as diabetes, multiple platform integrated profiling of metabolite alterations in biofluids can provide important insights into the processes underlying the disease.

Weighted least-squares deconvolution method for discovery of group differences between complex biofluid 1H NMR spectra.

Biomarker discovery through analysis of high-throughput NMR data is a challenging, time-consuming process due to the requirement of sophisticated, dataset specific preprocessing techniques and the inherent complexity of the data. Here, we demonstrate the use of weighted, constrained least-squares for fitting a linear mixture of reference standard data to complex urine NMR spectra as an automated way of utilizing current assignment knowledge and the ability to deconvolve confounded spectral regions. Following the least-squares fit, univariate statistics were used to identify metabolites associated with group differences. This method was evaluated through applications on simulated datasets and a murine diabetes dataset. Furthermore, we examined the differential ability of various weighting metrics to correctly identify discriminative markers. Our findings suggest that the weighted least-squares approach is effective for identifying biochemical discriminators of varying physiological states. Additionally, the superiority of specific weighting metrics is demonstrated in particular datasets. An additional strength of this methodology is the ability for individual investigators to couple this analysis with laboratory specific preprocessing techniques.

A video-based movement analysis system to quantify behavioral stress responses of fish.

Behavioral alterations can be measured as endpoints for sublethal toxicity, and serve as a tool for environmental risk assessment and analysis of toxicological impact. Numerous technical and biological factors have made sublethal effects on fish behavior difficult to quantify. In order to investigate stress- and contaminant-induced behavioral alterations, a video analysis system was designed by our laboratory. With this system up to 12 fish may be individually housed in 20 L exposure arenas and automatically videotaped at multiple and discrete intervals during an experimental period. Analog video data can then digitized, converted into x,y coordinates, and finally transformed into relevant behavioral endpoints using software designed for tracking fish movement combined with specific algorithms. These endpoints include velocity, total distance traveled, angular change, percent movement, space utilization, and fractal dimension (path complexity). Data from fish exposed to a reference toxicant, MS222, and simulation experiments, are presented to exemplify alterations in fish behavior associated with exposure, and accuracy and precision, respectively. The system provides flexibility to analyze any observed movement behavior, is remotely controlled, and can be transportable. These movement analyses can be used to identify characteristic behavioral responses to a variety of environmentally-relevant stressors, and assist in risk assessment and the development of more sensitive lowest observable effect level and no observable effect level for sentinel species.