Evidence-based annotation of the malaria parasite's genome using comparative expression profiling

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Abstract

A fundamental problem in systems biology and whole genome sequence analysis is how to infer functions for the many uncharacterized proteins that are identified, whether they are conserved across organisms of different phyla or are phylum-specific. This problem is especially acute in pathogens, such as malaria parasites, where genetic and biochemical investigations are likely to be more difficult. Here we perform comparative expression analysis on Plasmodium parasite life cycle data derived from P. falciparum blood, sporozoite, zygote and ookinete stages, and P. yoelii mosquito oocyst and salivary gland sporozoites, blood and liver stages and show that type II fatty acid biosynthesis genes are upregulated in liver and inserct stages relative to asexual blood stages. We also show that some universally uncharacterized genes with orthologs in Plasmodium species. Sacchromyces cerevisiae and human show coordinated transcription transcription patterns in large collections of human and yeast expression data and that the function of uncharacterized genes can sometimes be predicted based on the expression patterns across these diverse organisms. We also use a comprehensive and unblased literature mining method to predict which uncharacterized parasite-specific-genes are likely to have roles in process such as gliding mobility, host-cell interactions, sporozoite stage, or rhoptry function. These analyses, together with protein-protein interaction data, provide probabilistic models that predict the function of 926 uncharacterized malaria genes and also suggest that malaria parasites may provide a simple model system for the study of some human processes. These data also provide a foundation for further studies of transcriptional regulation in malaria parasite. © 2008 Zhou et al.

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Zhou, Y., Ramachandran, V., Kumar, K. A., Westenberger, S., Refour, P., Zhou, B., … Winzeler, E. A. (2008). Evidence-based annotation of the malaria parasite’s genome using comparative expression profiling. PLoS ONE, 3(2). https://doi.org/10.1371/journal.pone.0001570

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