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RNA-based biomarkers to understand and predict disease progression


RNA-based biomarkers are becoming increasingly important due to their stability and sensitivity of detection. This project will use in vitro, in vivo and clinical sample analysis based approaches to validate the role of microRNA and non-coding RNA-based biomarkers in predicting diabetes, cardiovascular disorders and / or the progression to related complications of human disease.


Associate Professor Anandwardhan Hardikar.

Research location

Camperdown - School of Public Health

Program type



Based on our preliminary assessment of human pancreas development carried out using SOLiD next generation sequencing platform, we identified a signature of microRNAs and small non-codingRNA molecules that are specific to insulin-producing beta cells in the islets of Langerhans. This formed the basis of the RAPID Study that we initiated. The RAPID study is an "RNA-based Analysis for Prediction of Islet Death" and involves validating a signature of microRNAs / noncoding(nc)RNAs in a set of 2548 clinical study samples from multiple cohorts. The aim of this study group is to assess the potential of ncRNA / microRNA based biomarkers for early diagnosis of diabetes and for the progression to complications. As of now, microRNA / ncRNA-based biomarkers have been identified in the area of cardiovascular and cancer biology by several researchers worldwide. These are being established into a clinical diagnostic tool. However, similar advances are lacking in the area of diabetes.

Most of the diabetic individuals who are clinically diagnosed for type 1 diabetes have more than 60% of their insulin-producing cells killed by the time they start showing clinical symptoms. There is therefore an urgent need to identify novel biomarkers that can help us diagnose the development of diabetes at very early stages of the disease.

The potential PhD student will be trained in skills related to in vitro and in vivo manipulations of human islet as well as vascular endothelial cells. Techniques related to this work involve routine TaqMan-based quantitative PCR, low-density arrays, small RNA sequencing, immune-staining and fluorescent in situ hybridization (ICC-FISH), confocal as well as laser catapulting microscopy, lineage tracing analyses, clinical sample analysis, bioinformatics and statistics. These studies would help in generating a solid piece of information on the suitability of such biomarkers for clinical testing as well as for prediction of disease progression and treatment options in diabetes. Hopefully the knowledge gained from these studies would inform medical researchers as to how to predict the development of Type 1 diabetes and the progression of Type 2 diabetes, provide tests to monitor treatment strategies and guide the development of new treatments to lessen the burden of diabetes and related complications.

Additional information

For more information related to this research please visit the RAPID Study group web page or the Hardikar lab website.

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Opportunity ID

The opportunity ID for this research opportunity is 1567