Gene expression and proteomic analysis of the formation of Phakopsora pachyrhizi appressoria.

BACKGROUND: Phakopsora pachyrhizi is an obligate fungal pathogen causing Asian soybean rust (ASR). A dual approach was taken to examine the molecular and biochemical processes occurring during the development of appressoria, specialized infection structures by which P. pachyrhizi invades a host plant. Suppression subtractive hybridization (SSH) was utilized to generate a cDNA library enriched for transcripts expressed during appressoria formation. Two-dimensional gel electrophoresis and mass spectroscopy analysis were used to generate a partial proteome of proteins present during appressoria formation. RESULTS: Sequence analysis of 1133 expressed sequence tags (ESTs) revealed 238 non-redundant ESTs, of which 53% had putative identities assigned. Twenty-nine of the non-redundant ESTs were found to be specific to the appressoria-enriched cDNA library, and did not occur in a previously constructed germinated urediniospore cDNA library. Analysis of proteins against a custom database of the appressoria-enriched ESTs plus Basidiomycota EST sequences available from NCBI revealed 256 proteins. Fifty-nine of these proteins were not previously identified in a partial proteome of P. pachyrhizi germinated urediniospores. Genes and proteins identified fell into functional categories of metabolism, cell cycle and DNA processing, protein fate, cellular transport, cellular communication and signal transduction, and cell rescue. However, 38% of ESTs and 24% of proteins matched only to hypothetical proteins of unknown function, or showed no similarity to sequences in the current NCBI database. Three novel Phakopsora genes were identified from the cDNA library along with six potentially rust-specific genes. Protein analysis revealed eight proteins of unknown function, which possessed classic secretion signals. Two of the extracellular proteins are reported as potential effector proteins. CONCLUSIONS: Several genes and proteins were identified that are expressed in P. pachyrhizi during appressoria formation. Understanding the role that these genes and proteins play in the molecular and biochemical processes in the infection process may provide insight for developing targeted control measures and novel methods of disease management.

T7 lytic phage-displayed peptide libraries exhibit less sequence bias than M13 filamentous phage-displayed peptide libraries.

We investigated whether the T7 system of phage display could produce peptide libraries of greater diversity than the M13 system of phage display due to the differing processes of lytic and filamentous phage morphogenesis. Using a bioinformatics-assisted computational approach, collections of random peptide sequences obtained from a T7 12-mer library (X(12)) and a T7 7-mer disulfide-constrained library (CX(7)C) were analyzed and compared with peptide populations obtained from New England BioLabs' M13 Ph.D.-12 and Ph.D.-C7C libraries. Based on this analysis, peptide libraries constructed with the T7 system have fewer amino acid biases, increased peptide diversity, and more normal distributions of peptide net charge and hydropathy than the M13 libraries. The greater diversity of T7-displayed libraries provides a potential resource of novel binding peptides for new as well as previously studied molecular targets. To demonstrate their utility, several of the T7-displayed peptide libraries were screened for streptavidin- and neutravidin-binding phage. Novel binding motifs were identified for each protein.

Looking for cancer clues in publicly accessible databases.

What started out as a mere attempt to tentatively identify proteins in experimental cancer-related 2D-PAGE maps developed into VIRTUAL2D, a web-accessible repository for theoretical pI/MW charts for 92 organisms. Using publicly available expression data, we developed a collection of tissue-specific plots based on differential gene expression between normal and diseased states. We use this comparative cancer proteomics knowledge base, known as the tissue molecular anatomy project (TMAP), to uncover threads of cancer markers common to several types of cancer and to relate this information to established biological pathways.

Tissue Molecular Anatomy Project (TMAP): an expression database for comparative cancer proteomics.

By mining publicly accessible databases, we have developed a collection of tissue-specific predictive protein expression maps as a function of cancer histological state. Data analysis is applied to the differential expression of gene products in pooled libraries from the normal to the altered state(s). We wish to report the initial results of our survey across different tissues and explore the extent to which this comparative approach may help uncover panels of potential biomarkers of tumorigenesis which would warrant further examination in the laboratory.

VIRTUAL2D: A web-accessible predictive database for proteomics analysis.

The available archive of sequence databases compiled from whole genome projects and budding proteomics efforts have enabled us to develop VIRTUAL2D, an interactive system for the assembly of virtual protein expression maps computed on the basis of theoretical isoelectric focusing point, molecular weight, tissue specificity and relative abundance for any set of proteins currently catalogued. This tool will assist in the preliminary, albeit putative, prediction of the identity and location of unknown and/or low abundance proteins in experimentally derived two-dimensional polyacrylamide gel electrophoresis maps.