Leveraging model legume information to find candidate genes for soybean sudden death syndrome using the legume information system.

Comparative genomics is an emerging and powerful approach to achieve crop improvement. Using comparative genomics, information from model plant species can accelerate the discovery of genes responsible for disease and pest resistance, tolerance to plant stresses such as drought, and enhanced nutritional value including production of anti-oxidants and anti-cancer compounds. We demonstrate here how to use the Legume Information System for a comparative genomics study, leveraging genomic information from Medicago truncatula (barrel medic), the model legume, to find candidate genes involved with sudden death syndrome (SDS) in Glycine max (soybean). Specifically, genetic maps, physical maps, and annotated tentative consensus and expressed sequence tag (EST) sequences from G. max and M. truncatula can be compared. In addition, the recently published M. truncatula genomic sequences can be used to identify M. truncatula candidate genes in a genomic region syntenic to a quantitative trait loci region for SDS in soybean. Genomic sequences of candidate genes from M. truncatula can then be used to identify ESTs with sequence similarities from soybean for primer design and cloning of potential soybean disease causing alleles.

Phytophthora functional genomics database (PFGD): functional genomics of phytophthora-plant interactions.

The Phytophthora Functional Genomics Database (PFGD; http://www.pfgd.org), developed by the National Center for Genome Resources in collaboration with The Ohio State University-Ohio Agricultural Research and Development Center (OSU-OARDC), is a publicly accessible information resource for Phytophthora-plant interaction research. PFGD contains transcript, genomic, gene expression and functional assay data for Phytophthora infestans, which causes late blight of potato, and Phytophthora sojae, which affects soybeans. Automated analyses are performed on all sequence data, including consensus sequences derived from clustered and assembled expressed sequence tags. The PFGD search filter interface allows intuitive navigation of transcript and genomic data organized by library and derived queries using modifiers, annotation keywords or sequence names. BLAST services are provided for libraries built from the transcript and genomic sequences. Transcript data visualization tools include Quality Screening, Multiple Sequence Alignment and Features and Annotations viewers. A genomic browser that supports comparative analysis via novel dynamic functional annotation comparisons is also provided. PFGD is integrated with the Solanaceae Genomics Database (SolGD; http://www.solgd.org) to help provide insight into the mechanisms of infection and resistance, specifically as they relate to the genus Phytophthora pathogens and their plant hosts.

Expressed sequence tags from the oomycete fish pathogen Saprolegnia parasitica reveal putative virulence factors.

BACKGROUND: The oomycete Saprolegnia parasitica is one of the most economically important fish pathogens. There is a dramatic recrudescence of Saprolegnia infections in aquaculture since the use of the toxic organic dye malachite green was banned in 2002. Little is known about the molecular mechanisms underlying pathogenicity in S. parasitica and other animal pathogenic oomycetes. In this study we used a genomics approach to gain a first insight into the transcriptome of S. parasitica. RESULTS: We generated 1510 expressed sequence tags (ESTs) from a mycelial cDNA library of S. parasitica. A total of 1279 consensus sequences corresponding to 525944 base pairs were assembled. About half of the unigenes showed similarities to known protein sequences or motifs. The S. parasitica sequences tended to be relatively divergent from Phytophthora sequences. Based on the sequence alignments of 18 conserved proteins, the average amino acid identity between S. parasitica and three Phytophthora species was 77% compared to 93% within Phytophthora. Several S. parasitica cDNAs, such as those with similarity to fungal type I cellulose binding domain proteins, PAN/Apple module proteins, glycosyl hydrolases, proteases, as well as serine and cysteine protease inhibitors, were predicted to encode secreted proteins that could function in virulence. Some of these cDNAs were more similar to fungal proteins than to other eukaryotic proteins confirming that oomycetes and fungi share some virulence components despite their evolutionary distance CONCLUSION: We provide a first glimpse into the gene content of S. parasitica, a reemerging oomycete fish pathogen. These resources will greatly accelerate research on this important pathogen. The data is available online through the Oomycete Genomics Database.

The Legume Information System (LIS): an integrated information resource for comparative legume biology.

The Legume Information System (LIS) (http://www.comparative-legumes.org), developed by the National Center for Genome Resources in cooperation with the USDA Agricultural Research Service (ARS), is a comparative legume resource that integrates genetic and molecular data from multiple legume species enabling cross-species genomic and transcript comparisons. The LIS virtual plant interface allows simplified and intuitive navigation of transcript data from Medicago truncatula, Lotus japonicus, Glycine max and Arabidopsis thaliana. Transcript libraries are represented as images of plant organs in different developmental stages, which are selected to query the analyzed and annotated data. Complex queries can be accomplished by adding modifiers, keywords and sequence names. The LIS also contains annotated genomic data featuring transcript alignments to validate gene predictions as well as motif and similarity analyses. The genomic browser supports comparative analysis via novel dynamic functional annotation comparisons. CMap, developed as part of the GMOD project (http://www.gmod.org/cmap/index.shtml), has been incorporated to support comparative analyses of community linkage and physical map data. LIS is being expanded to incorporate gene expression and biochemical pathways which will be seamlessly integrated forming a knowledge discovery framework.