Abstract
Cryptococcus neoformans is an opportunistic human pathogenic fungus that causes meningoencephalitis. Due to the increasing global risk of cryptococcosis and the emergence of drug-resistant strains, the development of predictive genetics platforms for the rapid identification of novel genes governing pathogenicity and drug resistance of C. neoformans is imperative. The analysis of functional genomics data and genome-scale mutant libraries may facilitate the genetic dissection of such complex phenotypes but with limited efficiency. Here, we present a genome-scale co-functional network for C. neoformans, CryptoNet, which covers ~81% of the coding genome and provides an efficient intermediary between functional genomics data and reverse-genetics resources for the genetic dissection of C. neoformans phenotypes. CryptoNet is the first genome-scale co-functional network for any fungal pathogen. CryptoNet effectively identified novel genes for pathogenicity and drug resistance using guilt-by-association and context-associated hub algorithms. CryptoNet is also the first genome-scale co-functional network for fungi in the basidiomycota phylum, as Saccharomyces cerevisiae belongs to the ascomycota phylum. CryptoNet may therefore provide insights into pathway evolution between two distinct phyla of the fungal kingdom. The CryptoNet web server (www.inetbio.org/cryptonet) is a public resource that provides an interactive environment of network-assisted predictive genetics for C. neoformans.
Highlights
Cryptococcus neoformans is an opportunistic human pathogenic fungus that causes meningoencephalitis
Log likelihood scores (LLS) based on a Bayesian statistical framework and weighted sum (WS) methods were used to integrate diverse types of data derived from three different species (C. neoformans, S. cerevisiae, and H. sapiens); these methods were used previously in the construction of the co-functional network for S. cerevisiae[13]
A total of 14 networks derived from C. neoformans specific data and orthology-based transfer data were integrated into a single network of C. neoformans genes, CryptoNet, which maps 156,506 co-functional links among 5,649 genes (i.e.,81% of coding genome)
Summary
Cryptococcus neoformans is an opportunistic human pathogenic fungus that causes meningoencephalitis. A systematic knockout library of 1,201 C. neoformans genes became available, and was used to identify novel genes relevant to virulence[9]. This mutant library, covers only 20% of the C. neoformans genome. The network-assisted genetic dissection of complex phenotypes has proven effective in a model fungus, Saccharomyces cerevisiae, using a genome-scale co-functional gene network, YeastNet[13,14]. Because the genetic principles of complex phenotypes are similar across fungal species, the network-assisted approach may facilitate the effective identification of novel genes for virulence and adaptation to chemical stresses in pathogenic fungi, including C. neoformans, provided that a high quality co-functional gene network becomes available. The transfer of potentially false links from other species can be minimized by the judicious weighting of links for pathogenic fungi
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