Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes
1 Department of Biochemistry & Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, 3010, Australia
2 Department of Veterinary Science, The University of Melbourne, 250 Princes Highway, Werribee, Victoria, 3030, Australia
3 Current address: Peter MacCallum Cancer Centre, St Andrew's Place, East Melbourne Victoria, 3002, Australia
BMC Genomics 2010, 11:222 doi:10.1186/1471-2164-11-222Published: 3 April 2010
New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset.
Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools.
The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.