Identifying novel disease-associated genes in Candida albicans

Using network-extracted ontologies to identify disease-associated pathways in the pathogenic yeast Candida albicans.

Candida albicans is an important human pathogen. It causes common human disease an in some cases can be life-threatening. Although we have some treatments for fungal infections caused by C. albicans, strains resistant to these treatments have been identified. Therefore, it is important to identify novel genes involved in disease processes that can be targeted for future medical intervention.

Using expression data from RNA-Sequencing experiments for C. albicans I have built networks that cluster genes with similar expression profiles. These clusters can be associated with disease using existing experimental data. This reveals clusters of genes involved in disease where unknwon genes represent potential new targets for medical intervention.

Novel targets identified from this analysis are currently being experimentally validated with collaborators at the Aberdeen Fungal Group.

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New bioinformatics tools for identifying novel fungicide targets

Developing new computational biology tools for the identification of fungicide targets in the crop pathogen Magnaporthe oryzae.

Pest and pathogens cause the loss of millions of tonnes of crops each year. Therefore, they are a significant cause of financial losses and also represent a threat to global food security.

The fungus Magnaporthe oryzae is a major pathogen of crops such as rice and wheat. As such, it causes huge losses of crops each year and is currently a key focus for biological reseach.

This project will focus on developing new computational tools to analyse M. oryzae sequence data to identify new targets for fungicides. Ultimately, this research, in collaboration with other research groups and industry partners, may lead to new effective treatments for this important pathogen.

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The evolution of pathogenicity in yeast

Using comparative network analysis to identify the events that led to the evolution of pathogenicity in yeast.

Yeast occupy a variety of environments from plants to insects and mammalian digestive tracts and also used in industry. Several yeast species are also important human pathogens. Despite these huge differences in natural environments pathogenic species can be closely related to non-pathogenic species. For example, Candida glabrata is a human pathogen and very closley related to the bakers yeast Saccharomyces cerevisiae. This suggests that minor genome changes in combination with changes in expression and interactions may underpin the evolution of pathogenicity.

Using comparative network approaches to compare the genomes, expression and protein interactions of pathogenic and non-pathogenic yeast I aim to infer the evolutionary events associated with pathogenicity. Early work on this project has involved the comparison of expression profiles between C. glabrata and S. cerevisiae. In the future this project will expand to incorporate more species and will include the development of new methods for representing and comparing biological networks.

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