Tristan E. Coram

Rapid transcriptome characterization and parsing of sequences in a non-model host-pathogen interaction; pea- Sclerotinia sclerotiorum

BackgroundWhite mold, caused by Sclerotinia sclerotiorum, is one of the most important diseases of pea (Pisum sativum L.), however, little is known about the genetics and biochemistry of this interaction. Identification of genes underlying resistance in the host or pathogenicity and virulence factors in the pathogen will increase our knowledge of the pea-S. sclerotiorum interaction and facilitate the introgression of new resistance genes into commercial pea varieties. Although the S. sclerotiorum genome sequence is available, no pea genome is available, due in part to its large genome size (~3500 Mb) and extensive repeated motifs. Here we present an EST data set specific to the interaction between S. sclerotiorum and pea, and a method to distinguish pathogen and host sequences without a species-specific reference genome.Results10,158 contigs were obtained by de novo assembly of 128,720 high-quality reads generated by 454 pyrosequencing of the pea-S. sclerotiorum interactome. A method based on the tBLASTx program was modified to distinguish pea and S. sclerotiorum ESTs. To test this strategy, a mixture of known ESTs (18,490 pea and 17,198 S. sclerotiorum ESTs) from public databases were pooled and parsed; the tBLASTx method successfully separated 90.1% of the artificial EST mix with 99.9% accuracy. The tBLASTx method successfully parsed 89.4% of the 454-derived EST contigs, as validated by PCR, into pea (6,299 contigs) and S. sclerotiorum (2,780 contigs) categories. Two thousand eight hundred and forty pea ESTs and 996 S. sclerotiorum ESTs were predicted to be expressed specifically during the pea-S. sclerotiorum interaction as determined by homology search against 81,449 pea ESTs (from flowers, leaves, cotyledons, epi- and hypocotyl, and etiolated and light treated etiolated seedlings) and 57,751 S. sclerotiorum ESTs (from mycelia at neutral pH, developing apothecia and developing sclerotia). Among those ESTs specifically expressed, 277 (9.8%) pea ESTs were predicted to be involved in plant defense and response to biotic or abiotic stress, and 93 (9.3%) S. sclerotiorum ESTs were predicted to be involved in pathogenicity/virulence. Additionally, 142 S. sclerotiorum ESTs were identified as secretory/signal peptides of which only 21 were previously reported.ConclusionsWe present and characterize an EST resource specific to the pea-S. sclerotiorum interaction. Additionally, the tBLASTx method used to parse S. sclerotiorum and pea ESTs was demonstrated to be a reliable and accurate method to distinguish ESTs without a reference genome.

Development and Characterization of 37 Novel EST-SSR Markers in Pisum sativum (Fabaceae)

Premise of the study: Simple sequence repeat markers were developed based on expressed sequence tags (EST-SSR) and screened for polymorphism among 23 Pisum sativum individuals to assist development and refinement of pea linkage maps. In particular, the SSR markers were developed to assist in mapping of white mold disease resistance quantitative trait loci. Methods and Results: Primer pairs were designed for 46 SSRs identified in EST contiguous sequences assembled from a 454 pyrosequenced transcriptome of the pea cultivar, ‘LIFTER’. Thirty-seven SSR markers amplified PCR products, of which 11 (30%) SSR markers produced polymorphism in 23 individuals, including parents of recombinant inbred lines, with two to four alleles. The observed and expected heterozygosities ranged from 0 to 0.43 and from 0.31 to 0.83, respectively. Conclusions: These EST-SSR markers for pea will be useful for refinement of pea linkage maps, and will likely be useful for comparative mapping of pea and as tools for marker-based pea breeding.

An improved algorithm for the detection of genomic variation using short oligonucleotide expression microarrays

High‐throughput microarray experiments often generate far more biological information than is required to test the experimental hypotheses. Many microarray analyses are considered finished after differential expression and additional analyses are typically not performed, leaving untapped biological information left undiscovered. This is especially true if the microarray experiment is from an ecological study of multiple populations. Comparisons across populations may also contain important genomic polymorphisms, and a subset of these polymorphisms may be identified with microarrays using techniques for the detection of single feature polymorphisms (SFP). SFPs are differences in microarray probe level intensities caused by genetic polymorphisms such as single‐nucleotide polymorphisms and small insertions/deletions and not expression differences. In this study, we provide a new algorithm for the detection of SFPs, evaluate the algorithm using existing data from two publicly available Affymetrix Barley (Hordeum vulgare) microarray data sets and compare them to two previously published SFP detection algorithms. Results show that our algorithm provides more consistent and sensitive calling of SFPs with a lower false discovery rate. Simultaneous analysis of SFPs and differential expression is a low‐cost method for the enhanced analysis of microarray data, enabling additional biological inferences to be made.