Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and PLS-DA.

Metabolic phenotyping, or metabotyping, is increasingly being used as a probe in functional genomics studies. However, such profiling is subject to intrinsic physiological variation found in all animal populations. Using a nuclear magnetic resonance-based metabonomic approach, we show that diurnal variations in metabolism can obscure the interpretation of strain-related metabolic differences in two phenotypically normal mouse strains (C57BL10J and Alpk:ApfCD). To overcome this problem, diurnal-related metabolic variation was removed from these spectral data by application of orthogonal signal correction (OSC), a data filtering method. Interpretation of the removed orthogonal variation indicated that diurnal-related variation had been removed and that the AM samples contained higher levels of creatine, hippurate, trimethylamine, succinate, citrate and 2-oxo-glutarate and lower levels of taurine, trimethylamine-N-oxide, spermine and 3-hydroxy-iso-valerate relative to the PM samples. We propose OSC will have great potential removing confounding variation obscuring subtle changes in metabolism in functional genomic studies and will be of benefit to optimising interpretation of proteomic and genomic datasets.