Isolation and partial characterization of a complementary protein from the mycoparasite, Piptocephalis virginiana, which specifically binds to two glycoproteins b and c of the host cell surface

Presence of surface glycoprotein in Piptocephalis virginiana that recognizes the host glycoproteins band c, reported earlier from our laboratory, was detected by immunofluorescence microscopy. Germinated spores of P. virginiana treated with Mortierella pusilla cell wall protein extract, primary antibodies prepared against glycoproteins band c and FITC-goat anti-rabbit IgG conjugate showed fluorescence. This indicated that on the surfaces of the biotrophic mycoparasite P. virginiana , there might be a complementary molecule which recognizes the glycoproteins band c from M. pusilla. Immunobinding analysis identified a glycoprotein of Mr 100 kDa from the mycoparasite which binds with the host glycoproteins band c, separately as well as collectively. Purification of this glycoprotein was achieved by (i) 60% ammonium sulfate precipitation, (ii) followed by heat treatment, and (iii) Sephadex G-IOO gel filtration. The glycoprotein was isolated by preparative polyacrylamide gel electrophoresis by cutting and elution. The purity of the protein ·was ascertained by SDS-PAGE and Western blot analysis. Positive reaction to periodic acid-Schiff reagent revealed the glycoprotein nature of this 100 kDa protein. Mannose was identified as a major sugar component of this glycoprotein by using a BoehringerMannheim Glycan Differentiation Kit. Electrophoretically purified glycoprotein was used to raIse polyclonal antibody in rabbit. The specificity of the antibody was determined by dot-immunobinding test and western-blot analysis. Immunofluorescence mIcroscopy revealed surface localization of the protein on the germ tube of Piptocephalis virginiana. Fluorescence was also observed at the surfaceJ of the germinated spores and hyphae of the host, M. pusilla after treatment with complementary protein from P. virginiana, primary antibody prepared against the complementary protein and FITC-goat anti-rabbit IgG conjugate.

Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.