Mark E. Waugh

Functional genomics of cell elongation in developing cotton fibers.

Cotton fibers are single-celled seed trichomes of major economic importance. Factors that regulate the rate and duration of cell expansion control fiber morphology and important agronomic traits. For genetic characterization of rapid cell elongation in cotton fibers, ∼ 14,000 unique genes were assembled from 46,603 expressed sequence tags (ESTs) from developmentally staged fiber cDNAs of a cultivated diploid species (Gossypium arboreumL.). Conservatively, the fiber transcriptome represents 35–40% of the genes in the cotton genome. In silico expression analysis revealed that rapidly elongating fiber cells exhibit significant metabolic activity, with the bulk of gene transcripts, represented by three major functional groups – cell wall structure and biogenesis, the cytoskeleton and energy/carbohydrate metabolism. Oligonucleotide microarrays revealed dynamic changes in gene expression between primary and secondary cell wall biogenesis showing that fiber genes in the dbEST are highly stage-specific for cell expansion – a conclusion supported by the absence of known secondary cell wall-specific genes from our fiber dbEST. During the developmental switch from primary to secondary cell wall syntheses, 2553 “expansion-associated” fiber genes are significantly down regulated. Genes (81) significantly up-regulated during secondary cell wall synthesis are involved in cell wall biogenesis and energy/carbohydrate metabolism, which is consistent with the stage of cellulose synthesis during secondary cell wall modification in developing fibers. This work provides the first in-depth view of the genetic complexity of the transcriptome of an expanding cell, and lays the groundwork for studying fundamental biological processes in plant biology with applications in agricultural biotechnology.

Large-Scale Gene Discovery in the Oomycete Phytophthora infestans Reveals Likely Components of Phytopathogenicity Shared with True Fungi

To overview the gene content of the important pathogen Phytophthora infestans, large-scale cDNA and genomic sequencing was performed. A set of 75,757 high-quality expressed sequence tags (ESTs) from P. infestans was obtained from 20 cDNA libraries representing a broad range of growth conditions, stress responses, and developmental stages. These included libraries from P. infestans-potato and -tomato interactions, from which 963 pathogen ESTs were identified. To complement the ESTs, onefold coveragethe P. infestans genome was obtained and regions of coding potential identified. A unigene set of 18,256 sequences was derived from the EST and genomic data and characterized for potential functions, stage-specific patterns of expression, and codon bias. Cluster analysis of ESTs revealed major differences between the expressed gene content of mycelial and spore-related stages, and affinities between some growth conditions. Comparisons with databases of fungal pathogenicity genes revealed conserved elements of pa...

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.

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

BackgroundThe 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.ResultsWe 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 distanceConclusionWe 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 [1].

Expressed Sequence Tags from Phytophthora sojae Reveal Genes Specific to Development and Infection

Six unique expressed sequence tag (EST) libraries were generated from four developmental stages of Phytophthora sojae P6497. RNA was extracted from mycelia, swimming zoospores, germinating cysts, and soybean (Glycine max (L.) Merr.) cv. Harosoy tissues heavily infected with P. sojae. Three libraries were created from mycelia growing on defined medium, complex medium, and nutrient-limited medium. The 26,943 high-quality sequences obtained clustered into 7,863 unigenes composed of 2,845 contigs and 5,018 singletons. The total number of P. sojae unigenes matching sequences in the genome assembly was 7,412 (94%). Of these unigenes, 7,088 (90%) matched gene models predicted from the P. sojae sequence assembly, but only 2,047 (26%) matched P. ramorum gene models. Analysis of EST frequency from different growth conditions and morphological stages revealed genes that were specific to or highly represented in particular growth conditions and life stages. Additionally, our results indicate that, during infection, the pathogen derives most of its carbon and energy via glycolysis of sugars in the plant. Sequences identified with putative roles in pathogenesis included avirulence homologs possessing the RxLR motif, elicitins, and hydrolytic enzymes. This large collection of P. sojae ESTs will serve as a valuable public genomic resource.

GeneX: an open source gene expression database and integrated tool set

Because gene expression profiles are highly sensitive to sample and processing conditions, it is crucial to accurately represent these conditions along with the numeric data in a way that allows the conditions to be part of a query. The GeneXTM project is intended to provide an Open Source database and integrated tool set that will allow researchers to store and evaluate their gene expression data and, moreover, such evaluation will be independent of the technology used to obtain the data.

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

The Phytophthora Functional Genomics Database (PFGD; ), 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; ) to help provide insight into the mechanisms of infection and resistance, specifically as they relate to the genus Phytophthora pathogens and their plant hosts.

The Genome Sequence DataBase (GSDB): improving data quality and data access.

In 1997 the primary focus of the Genome Sequence DataBase (GSDB; www. ncgr.org/gsdb ) located at the National Center for Genome Resources was to improve data quality and accessibility. Efforts to increase the quality of data within the database included two major projects; one to identify and remove all vector contamination from sequences in the database and one to create premier sequence sets (including both alignments and discontiguous sequences). Data accessibility was improved during the course of the last year in several ways. First, a graphical database sequence viewer was made available to researchers. Second, an update process was implemented for the web-based query tool, Maestro. Third, a web-based tool, Excerpt, was developed to retrieve selected regions of any sequence in the database. And lastly, a GSDB flatfile that contains annotation unique to GSDB (e.g., sequence analysis and alignment data) was developed. Additionally, the GSDB web site provides a tool for the detection of matrix attachment regions (MARs), which can be used to identify regions of high coding potential. The ultimate goal of this work is to make GSDB a more useful resource for genomic comparison studies and gene level studies by improving data quality and by providing data access capabilities that are consistent with the needs of both types of studies.

Classification of hepatitis C virus and human immunodeficiency virus-1 sequences with the branching index

Classification of viral sequences should be fast, objective, accurate and reproducible. Most methods that classify sequences use either pair-wise distances or phylogenetic relations, but cannot discern when a sequence is unclassifiable. The branching index (BI) combines distance and phylogeny methods to compute a ratio that quantifies how closely a query sequence clusters with a subtype clade. In the hypothesis-testing framework of statistical inference, the BI is compared with a threshold to test whether sufficient evidence exists for the query sequence to be classified among known sequences. If above the threshold, the null hypothesis of no support for the subtype relation is rejected and the sequence is taken as belonging to the subtype clade with which it clusters on the tree. This study evaluates statistical properties of the BI for subtype classification in hepatitis C virus (HCV) and human immunodeficiency virus-1 (HIV-1). Pairs of BI values with known positive- and negative-test results were computed from 10,000 random fragments of reference alignments. Sampled fragments were of sufficient length to contain phylogenetic signals that grouped reference sequences together properly into subtype clades. For HCV, a threshold BI of 0.71 yields 95.1% agreement with reference subtypes, with equal false-positive and false-negative rates. For HIV-1, a threshold of 0.66 yields 93.5% agreement. Higher thresholds can be used where lower false-positive rates are required. In synthetic recombinants, regions without breakpoints are recognized accurately; regions with breakpoints do not represent any known subtype uniquely. Web-based services for viral subtype classification with the BI are available online.