Advancing our understanding of Candida albicans pathobiology by Barseq omics technologies

Supervisors: Carol Munro, Elaina Collie-Duguid, Gowri Sripada

Project description:

The Candida albicans genome was sequenced over a decade ago [1] and despite the efforts of laboratories worldwide to assign function to genes, around 70% of the genome remains uncharacterised. A deeper understanding of the pathobiology of this important pathogen is required in order to design better ways of detecting and combatting this life-threatening fungus. To improve C. albicans functional genomics we are generating a genome-wide library of overexpression strains. Each strain is tagged with a unique molecular barcode for next generation bar-code sequencing (BarSeq). Pools of mutants can be screened in high-throughput assays for particular virulence-related phenotypes [2], and for suppressor or complementation assays [3].

The main objective of the project is to gain a better understanding of the genes and pathways that contribute to fitness under conditions that mimic growth in host niches. Fitness of pools of the overexpression mutants will be measured by quantifying the mutants present at the end of the growth assays using BarSeq screening of mutant pools. This will provide information on the mutants that are more or less abundant in the pools compared to a control strain. By testing fitness in a number of different growth conditions, including treatment with antifungal drugs, we can identify the genes required for growth under those conditions. The interdisciplinary component is the network analysis and data visualization that will then be performed with the expertise of Dr Sripada. Computational analysis will be applied to associate genes identified in the BarSeq screening with genes known to demonstrate similar phenotypes. Networks will be constructed allowing us to visualise and interrogate the large datasets generated.  This analysis will link uncharacterised genes with genes of known function and by association we can start to infer functions of the uncharacterised genes. We can then generate testable hypotheses that specific genes are required for growth under particular conditions. We will test the hypotheses for up to 12 genes by generating null mutants and testing their virulence in invertebrate and murine infection models.

The proposal will utilise the genomics and bioinformatics expertise of the Centre for Genome Enabled Biology and Medicine (CGEBM) and next generation sequencing technology. Dr Sripada will provide expertise in gene association analysis, network construction, incorporation of data into online database (EuPathDB) and data visualisation tools. The student will be given training in sequence analysis, bioinformatics, network construction and data visualisation. They will also be given training in medical mycology, fungal cell biology and microbiology techniques as well as molecular biology, mutant construction and infection models.

The predicted outcome of the project will be a significant advancement of our understanding of fungal pathobiology and an improvement in the C. albicans genome annotation by associating genes of unknown function with specific pathways and processes.


[1] Braun et al., (2005) PloS Genetics 1:36-57. [2] Cabral et al., (2014) PLoS Pathogens 10(12):e1004542. [3] Chauvel et al., (2012) PLoS One 7( 9):e45912.