A method for the analysis of sugars in biological systems using reductive amination in combination with hydrophilic interaction chromatography and high resolution mass spectrometry.

Separation of sugar isomers extracted from biological samples is challenging because of their natural occurrence as alpha and beta anomers and, in the case of hexoses, in their pyranose and furanose forms. A reductive amination method was developed for the tagging of sugars with the aim of it becoming part of a metabolomics work flow. The best separation of the common hexoses (glucose, fructose, mannose and galactose) was achieved when 2H5-aniline was used as the tagging reagent in combination with separation on a ZICHILIC column. The method was used to tag a range of sugars including pentoses and uronic acids. The method was simple to perform and was able to improve both the separation of sugars and their response to electrospray ionisation. The method was applied to the profiling of sugars in urine where a number of hexose and pentose isomers could be observed. It was also applied to the quantification of sugars in post-mortem brain samples from three control samples and three samples from individuals who had suffered from bipolar disorder.

Metabolomic Profiling of Submaximal Exercise at a Standardised Relative Intensity in Healthy Adults.

Ten physically active subjects underwent two cycling exercise trials. In the first, aerobic capacity (VO2max) was determined and the second was a 45 min submaximal exercise test. Urine samples were collected separately the day before (day 1) , the day of (day 2), and the day after (day 3) the submaximal exercise test (12 samples per subject). Metabolomic profiling of the samples was carried out using hydrophilic interaction chromatography (HILIC) coupled to an Orbitrap Exactive mass spectrometer. Data were extracted, database searched and then subjected to principle components (PCA) and orthogonal partial least squares (OPLSDA) modelling. The best results were obtained from pre-treating the data by normalising the metabolites to their mean output on days 1 and 2 of the trial. This allowed PCA to separate the day 2 first void samples (D2S1) from the day 2 post-exercise samples (D2S3) PCA also separated the equivalent samples obtained on day 1 (D1S1 and D1S3). OPLSDA modelling separated both the D2S1 and D2S3 samples and D1S1 and D1S3 samples. The metabolites affected by the exercise samples included a range of purine metabolites and several acyl carnitines. Some metabolites were subject to diurnal variation these included bile acids and several amino acids, the variation of these metabolites was similar on day 1 and day 2 despite the exercise intervention on day 2. Using OPLS modelling it proved possible to identify a single abundant urinary metabolite provisionally identified as oxo-aminohexanoic acid (OHA) as being strongly correlated with VO2max when the levels in the D2S3 samples were considered.