No abstract

Because less than one-third of clinically relevant fusaria can be accurately identified to species level using phenotypic data (i.e., morphological species recognition), we constructed a three-locus DNA sequence database to facilitate molecular identification of the 69 Fusarium species associated with human or animal mycoses encountered in clinical microbiology laboratories. The database comprises partial sequences from three nuclear genes: translation elongation factor 1␣ (EF-1␣), the largest subunit of RNA polymerase (RPB1), and the second largest subunit of RNA polymerase (RPB2). These three gene fragments can be amplified by PCR and sequenced using primers that are conserved across the phylogenetic breadth of Fusarium. Phylogenetic analyses of the combined data set reveal that, with the exception of two monotypic lineages, all clinically relevant fusaria are nested in one of eight variously sized and strongly supported species complexes. The monophyletic lineages have been named informally to facilitate communication of an isolate's clade membership and genetic diversity. To identify isolates to the species included within the database, partial DNA sequence data from one or more of the three genes can be used as a BLAST query against the database which is Web accessible at FUSARIUM-ID (http://isolate.fusariumdb.org) and the Centraalbureau voor Schimmelcultures (CBS-KNAW) Fungal Biodiversity Center (http://www.cbs.knaw.nl/fusarium). Alternatively, isolates can be identified via phylogenetic analysis by adding sequences of unknowns to the DNA sequence alignment, which can be downloaded from the two aforementioned websites. The utility of this database should increase significantly as members of the clinical microbiology community deposit in internationally accessible culture collections (e.g., CBS-KNAW or the Fusarium Research Center) cultures of novel mycosis-associated fusaria, along with associated, corrected sequence chromatograms and data, so that the sequence results can be verified and isolates are made available for future study.

Rapid translation of genome sequences into meaningful biological information hinges on the integration of multiple experimental and informatics methods into a cohesive platform. Despite the explosion in the number of genome sequences available, such a platform does not exist for filamentous fungi. Here we present the development and application of a functional genomics and informatics platform for a model plant pathogenic fungus, Magnaporthe oryzae. In total, we produced 21,070 mutants through large-scale insertional mutagenesis using Agrobacterium tumefaciens-mediated transformation. We used a high-throughput phenotype screening pipeline to detect disruption of seven phenotypes encompassing the fungal life cycle and identified the mutated gene and the nature of mutation for each mutant. Comparative analysis of phenotypes and genotypes of the mutants uncovered 202 new pathogenicity loci. Our findings demonstrate the effectiveness of our platform and provide new insights on the molecular basis of fungal pathogenesis. Our approach promises comprehensive functional genomics in filamentous fungi and beyond.

BackgroundFungi secrete various proteins that have diverse functions. Prediction of secretory proteins using only one program is unsatisfactory. To enhance prediction accuracy, we constructed Fungal Secretome Database (FSD).DescriptionA three-layer hierarchical identification rule based on nine prediction programs was used to identify putative secretory proteins in 158 fungal/oomycete genomes (208,883 proteins, 15.21% of the total proteome). The presence of putative effectors containing known host targeting signals such as RXLX [EDQ] and RXLR was investigated, presenting the degree of bias along with the species. The FSD's user-friendly interface provides summaries of prediction results and diverse web-based analysis functions through Favorite, a personalized repository.ConclusionsThe FSD can serve as an integrated platform supporting researches on secretory proteins in the fungal kingdom. All data and functions described in this study can be accessed on the FSD web site at http://fsd.snu.ac.kr/.

All data described in this study can be browsed through the FTFD web site at http://ftfd.snu.ac.kr/.