Thesis overview

This thesis describes analytical methods for the detection of metabolic biomarkers of disease and physiological change. The research was directed towards the development and application of novel analytical techniques, based on mass spectrometry and ion mobility spectrometry, and bioinformatics strategies for the identification of diagnostic/prognostic biomarkers. Methods have been developed based on UHPLC-MS alone and in combination with ion mobility spectrometry (UHPLC-IM-MS) for untargeted metabolite profiling of biofluids such as saliva and mouse plasma. The applications of these methods include investigations on biomarkers of the efficacy of colorectal cancer preventative treatment, diagnostic biomarkers of asthma and physiological stress.

The individual chapters are summarised in the following sections and directions for further work are proposed.

5.1.1 Summary of chapter one

Chapter one covers the theoretical principles of ion mobility spectrometry (IMS), mass spectrometry (MS) and multivariate statistical analysis (MVA) techniques. An account of modern day metabolomics, physiology and the application of saliva as a diagnostic medium is presented. The chapter also provides a review of analytical techniques used in metabolite profiling of biofluids with particular emphasis on salivary metabolite profiling.

5.1.2 Summary of chapter two

Chapter two demonstrates the potential of tri-wave ion mobility spectrometry in combination with ultra-high performance liquid chromatography and mass spectrometry for the metabolic analysis of mouse plasma. Untargeted metabolite profiling analysis of mouse plasma was carried out with minimal sample pre-treatment, involving only protein precipitation, to assess the chemopreventive efficacy of rice bran in colorectal cancer. Plasma derived from APCmin mice, a well-established model for colon carcinogenesis, was used for untargeted metabolite profiling and potential biomarker discovery. The UHPLC-IM-MS method was shown to yield improvements compared to conventional metabolite profiling methods based on HPLC-MS in the following manner:

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 The UHPLC-IM-MS method offered increased analytical space for untargeted metabolite profiling of mice plasma. This was achieved by a multi-dimensional separation of analytes based on UHPLC retention times, ion mobility and mass- to-charge ratios in a single 10 minute chromatographic run.

 The cycle time for the analysis of samples was 15 minutes with column clean-up and conditioning phases built into the UHPLC gradient method, which is ideal for high throughput analysis of metabolomic samples.

 The improvement of mass spectral data quality obtained by the removal of polyethylene glycol (PEG) contamination from the samples using post- acquisition processing was successfully demonstrated.

 Potential biomarker ions of efficacy of rice bran supplementation in colorectal cancer were identified.

5.1.3 Summary of chapter three

Chapter three presents the development and application of a robust methodology based on UHPLC-MS for salivary metabolite profiling and the discovery of potential biomarkers of asthma. The specific outcomes from this study are:

 A method has been developed and established for metabolite profiling of saliva using UHPLC-MS.

 The analytical method has been characterised (i.e. validated) on the basis of the reproducibility and dynamic range of the method.

 Unsupervised multivariate data analysis methods have been utilised after logical data reduction procedures to reduce the complexity of the very large dataset obtained from untargeted metabolite profiling studies.

 Ten potential metabolite biomarker ions of asthma have been identified from the moderate asthmatics in the study with their tentative elemental composition assignments.

 A predictive model based on partial least squares – discriminant analysis (PLS- DA) has been constructed using the ten discriminant ions, which shows good predictive capability for moderate asthmatics and controls. The predictive model was used to test its ability to classify mild asthmatic population.

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 Current methods for diagnosis of asthma - FEV1%, methacholine challenge and blood eosinophil level - all use invasive sample collection methods and thus cause patient discomfort. The developed method has been proposed to be used in conjunction with existing methods or as a preliminary screen for the diagnosis of asthma as passive drool sampling of saliva is carried out non-invasively.

5.1.4 Summary of chapter four

The potential of ion mobility in combination with existing ultra-high performance liquid chromatography and mass spectrometry methods for the untargeted metabolite profiling of saliva is demonstrated in Chapter four. The method developed is based on protein precipitation and UHPLC-IM-MS, along with multivariate data processing strategies for global metabolite profiling of saliva. The specific outcomes from the study were:

 The developed method utilises <1 mL volume of saliva (i.e. 500 µL), which is easily obtainable by passive drool method. The sample pre-treatment involves only protein precipitation as a clean-up stage.

 The chromatographic gradient for UHPLC-IM-MS analysis of saliva was optimised while maintaining the analysis cycle time at 15 minutes, which is advantageous for high throughput metabolite profiling analyses.

 Ion mobility (IM) separation was optimised for untargeted salivary metabolite profiling by UHPLC-IM-MS.

 The developed UHPLC-IM-MS method was characterised (i.e. validated) by assessing the reproducibility and dynamic range of the analytical method using endogenous metabolite ions thus accounting for matrix effects. These factors are important considerations for identification of up-regulated or down-regulated metabolite ions.

 The advantages of using ion mobility for targeted analysis and its advantages for enhancing the selectivity of target metabolites have been illustrated.

 The developed method has been successfully applied to a metabolite profiling study to discover potential biomarker ions of physiological stress.

 Valerolactam has been identified as a potential biomarker of physiological stress from saliva by comparison of retention time (tR), ion mobility drift time and MS/MS spectra with purchased standard of δ-valerolactam.

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5.2 FUTURE DIRECTIONS