Postdoctoral research fellow, The Walter and Eliza Hall Institute of Medical Research
Identifying blood-based diagnostic markers of disease
My research specialises in the field of proteomics- the study of proteins at work in the body. It provides a window into the microworld of our bodies. Using a cutting-edge technique called mass spectrometry, I can measure thousands of proteins at once in the blood to uncover unique signatures that can be used to identify hard-to-diagnose diseases. So far, I have applied this technique to Parkinson’s disease, a devastating central nervous system disease that progressively sees people lose control of their muscle movements, and a less well-known disease called acute rheumatic fever, or ARF. Acute rheumatic fever is a really significant problem in Aboriginal and Torres Strait Islander populations of Australia. It starts with a simple bacterial infection that causes a sore throat – called strep throat – but ends with the immune system attacking the heart valves, leaving them irreparably damaged. And it disproportionately affects young kids, with many needing multiple open-heart surgeries before the age of 20.
Acute rheumatic fever is a disease of disadvantage, and the incidence in Aboriginal Australians and Torres Strait Islanders are amongst the highest in the world, and most cases are found in children aged 5-14 years. Unless it is well-managed, it results in severe heart disease and death in early adulthood. Despite a clear need, there is currently no clear diagnostic test for either acute rheumatic fever or Parkinson’s disease. My goal is to develop a cheap and reliable blood test for these, and other, diseases using the cutting-edge technique that I developed, which would dramatically change the outcomes for people with these diseases.
My aim is to progress my research to a stage where it can be used in the clinic to provide a cheap, quick and reliable test for Parkinson’s disease and acute rheumatic fever that is minimally invasive – just a finger-prick, similar to genetic screening tests of newborn babies.
As with many modern science techniques, this research generates large amounts of data that require sophisticated analytical techniques such as ‘machine learning’ so the computer can discover patterns in the data that distinguish between healthy individuals and those with disease. We are currently building up our team to include more data scientists trained in how to gain new insights from ‘big data’.
Abstract: The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography–mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.
Pub.: 04 Mar '16, Pinned: 28 Jul '17
Abstract: Using global liquid chromatography-mass spectrometry (LC-MS)-based proteomics analyses, we identified 24 serum proteins that were significantly variant between those with type 1 diabetes (T1D) and healthy controls. Functionally, these proteins represent innate immune responses, the activation cascade of complement, inflammatory responses, and blood coagulation. Targeted verification analyses were performed on 52 surrogate peptides representing these proteins, with serum samples from an antibody standardization program cohort of 100 healthy control and 50 type 1 diabetic subjects. 16 peptides were verified as having very good discriminating power, with areas under the receiver operating characteristic curve ≥ 0.8. Further validation with blinded serum samples from an independent cohort (10 healthy control and 10 type 1 diabetics) demonstrated that peptides from platelet basic protein and C1 inhibitor achieved both 100% sensitivity and 100% specificity for classification of samples. The disease specificity of these proteins was assessed using sera from 50 age-matched type 2 diabetic individuals, and a subset of proteins, C1 inhibitor in particular, were exceptionally good discriminators between these two forms of diabetes. The panel of biomarkers distinguishing those with T1D from healthy controls and those with type 2 diabetes suggests that dysregulated innate immune responses may be associated with the development of this disorder.
Pub.: 02 Jan '13, Pinned: 28 Jul '17
Abstract: Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.
Pub.: 28 Jun '16, Pinned: 28 Jul '17