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dc.contributor.authorKeung, Karen Lok Yee
dc.date.accessioned2019-09-10
dc.date.available2019-09-10
dc.date.issued2019-02-28
dc.identifier.urihttp://hdl.handle.net/2123/21050
dc.description.abstractThis research aimed to address two unmet clinical needs in renal transplantation. Firstly, novel therapeutics to improve allograft outcomes are required. Secondly, non-invasive prognostic biomarkers to predict adverse allograft outcomes are needed to provide an early treatment opportunity. The use of large-scale molecular profiling data coupled with advanced computational strategies to address these needs was the foundation for this research. In chapters 3 and 4, the aim was to describe for the first time the derivation of master regulatory genes, or key drivers, amongst the pathologic molecular pathways in acute rejection (AR) of the renal allograft, and demonstrate that these may serve as novel targets for therapeutic intervention. Using microarray gene expression datasets from human renal allograft biopsy tissue, an integrative network-based computational approach was employed to predict the key driver genes in AR, identifying 14 key drivers. Interrogation of a computational drug-repositioning resource identified drugs in current clinical use as candidates for repositioning in AR prevention. Minocycline was selected for validation in a murine cardiac allograft model of AR. Used alone, it attenuated the inflammatory profile of AR compared with controls, and prolonged graft survival when co-administered with immunosuppression. The aim of the work in chapter 5 was to facilitate the discovery of a whole blood, thus non-invasive, prognostic biomarker signature obtained early in the post-transplant course that could predict patients who will develop allograft fibrosis. Early identification of these patients could provide an opportunity to alter immunosuppression before fibrosis is established. Here, validation of an unpublished prognostic microRNA signature derived from whole blood obtained 3 months post-transplant, to predict those with allograft fibrosis 12 months post-transplant, was sought. This signature was derived from collaborators in the Genomics of Chronic Allograft Dysfunction (GoCAR) study, and validation sought using samples from the Australian Chronic Allograft Dysfunction (AUSCAD) study, a single-centre cohort of transplant patients with prospective collection of renal biopsy and biofluid samples with linked clinical data. The 4-miRNA signature was unable to accurately prognosticate patients that progressed to fibrosis at 12 months in the AUSCAD cohort. How future work could be modified to facilitate the identification and validation of a robust prognostic biomarker signature are addressed.en_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectKidneyen_AU
dc.subjectTransplantationen_AU
dc.subjectacute rejectionen_AU
dc.subjecttranscriptomicsen_AU
dc.subjectdrug repositioningen_AU
dc.titleThe Application of Transcriptomics in Kidney Transplant Injuryen_AU
dc.typeThesisen_AU
dc.type.thesisDoctor of Philosophyen_AU
usyd.facultyFaculty of Medicine and Health, Westmead Clinical Schoolen_AU
usyd.degreeDoctor of Philosophy Ph.D.en_AU
usyd.awardinginstThe University of Sydneyen_AU


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