A systems-based functional genomics approach to understand schizophrenia and to identify disease biomarkers

16 Aug 2005

Sudhakaran Prabakaran
Institute of Biotechnology
University of Cambridge


The aim of the thesis is to take a fresh and unbiased look at schizophrenia. I used several "omics" approaches to characterise abnormalities at the protein and lipid level in brain and peripheral disease tissue. My hope was to increase general disease understanding and in turn identify potential diagnostic markers for the disease. Numerous hypotheses have been suggested since the last century but many of them have not been found to be consistent and reproducible. One of the main causes for the misinterpretations of this complex psychiatric disorder are unreliable/un-matched and underpowered sample sets, non-high throughput technologies, and most importantly the fixation on "pet-hypotheses". Another main hindrance to the study of schizophrenia is lack of distinct biomarker; hence the disease diagnosis is usually based on the subjective, interview-based impression of the clinician.

In this work, I (along other members of our team) have used a brute-force approach (exploring as many possible avenues to empirically define the disorder, before arriving at a hypothesis). This approach is now called a "systems-based approach" to understand this disease. Our group has employed multiple "omic" platforms; such as genomics, proteomics, lipidomics, metabolomics, metallomics to identify abnormalities in pre-frontal cortex area of the brain (the area of the brain which is known to play an important role in the schizophrenia disease process and cognition). My work is focused on proteomics, lipidomics and metallomics investigations. All data sets are being analysed in a statisitcally and bioinformatically rigorous fashion and subsequently integrated in a relational database. By doing so, I have identified biomarkers of disease.

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