Identification of Pathogenicity-Related Genes in the Vascular Wilt Fungus Verticillium dahliae by Agrobacterium tumefaciens-Mediated T-DNA Insertional Mutagenesis

Verticillium dahliae is the causal agent of vascular wilt in many economically important crops worldwide. Identification of genes that control pathogenicity or virulence may suggest targets for alternative control methods for this fungus. In this study, Agrobacteriumtumefaciens-mediated transformation (ATMT) was applied for insertional mutagenesis of V. dahliae conidia. Southern blot analysis indicated that T-DNAs were inserted randomly into the V. dahliae genome and that 69% of the transformants were the result of single copy T-DNA insertion. DNA sequences flanking T-DNA insertion were isolated through inverse PCR (iPCR), and these sequences were aligned to the genome sequence to identify the genomic position of insertion. V. dahliae mutants of particular interest selected based on culture phenotypes included those that had lost the ability to form microsclerotia and subsequently used for virulence assay. Based on the virulence assay of 181 transformants, we identified several mutant strains of V. dahliae that did not cause symptoms on lettuce plants. Among these mutants, T-DNA was inserted in genes encoding an endoglucanase 1 (VdEg-1), a hydroxyl-methyl glutaryl-CoA synthase (VdHMGS), a major facilitator superfamily 1 (VdMFS1), and a glycosylphosphatidylinositol (GPI) mannosyltransferase 3 (VdGPIM3). These results suggest that ATMT can effectively be used to identify genes associated with pathogenicity and other functions in V. dahliae

Evolutionary Modeling and Prediction of Non-Coding RNAs in Drosophila

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3′ end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA