Hanhae Kim1*, Kwang-Woo Jung2*, Shinae Maeng2, Ying-Lien Chen3,4, Junha Shin1, Jung Eun Shim1, Sohyun Hwang1, Guilhem Janbon5, Taeyup Kim3, Joseph Heitman3, Yong-Sun Bahn2 & Insuk Lee1
1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea, 2Department of Biotechnology, Center for Fungal Pathogenesis, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea, 3Department of Molecular Genetics and Microbiology, Medicine, and Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA, 4Department of Plant Pathology and Microbiology, National Taiwan University, Taipei, Taiwan, 5Institut Pasteur, Unite´ Biologie et Pathoge´nicite´ Fongiques, De´partement de Mycologie, F-75015, Paris, France.
Correspondence and requests for materials should be addressed to Yong-Sun Bahn or Insuk Lee
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.