Date of Award

2009

Degree Type

Thesis

Degree Name

Bachelor of Science Honours

School

School of Natural Sciences

Faculty

Faculty of Computing, Health and Science

First Advisor

Dr Mary Boyce

Second Advisor

Robert Trengove

Abstract

Metabolomics is complementary to genomics, transcriptomics and proteomics; however, it has the capacity to reflect the activities of the organism at a functional level. Metabolomics can therefore be used as a diagnostic tool by identifying the up- or down-regulation of metabolites in the cell, tissue, plasma, serum or urine. Specifically, these are, but are not limited to, identifying biomarkers of disease, monitoring drug treatments, and monitoring surgical procedures such as organ transplant. Autosomal recessive polycystic kidney disease (ARPKD) makes up 5-8% of patients requiring kidney dialysis and/or transplantation and of these, an estimated 50% of patients progress to end-stage renal disease (ESRD) by the age of 10 years, resulting in renal and liver-related morbidity and mortality. The purpose of this research was to utilise the Lewis Polycystic Kidney (LPK) rat to investigate the ARPKD phenotype using metabolomics. Spot urine samples were collected from 7 male Lewis; 8 male LPK; and 6 female LPK rats aged 5 weeks. Metabolites were extracted from urine and derivatised for GC/MS analysis. The peak area of target components was normalised to the internal standard ribitol and then to creatinine. Principal component analysis (PCA) was used to visualise sample grouping and the loadings plot of the PCA was used to determine key metabolites attributed to the variance between sample groups. α-ketoglutarate, uric acid and allantoin were proposed as potential biomarkers for ARPKD in the 5-week old male LPK rat. The findings of this study, particularly the development of a GC/MS method to analyse Lewis and LPK rat urine, demonstrate the potential of metabolomics to further investigate ARPKD.

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