2010 Annual Report
1a.Objectives (from AD-416)
Objective 1: Identify and evaluate genes important for agronomic performance (e.g., symbiosis/nitrogen fixation, nutrient uptake and utilization, yield, composition, etc.).
Objective 2: Identify and evaluate genes useful for legume defense against pathogens, e.g., Asian Soybean Rust.
Objective 3: Conduct comparative analyses of legume genes and genomes to place agronomically important genes in evolutionary and genome sequence context.
1b.Approach (from AD-416)
The project will define and characterize the organization and structure of the soybean genome and the genomes of other legumes with special emphasis on genes and gene families that underlie important agronomic and developmental traits. Hydroponics and global gene expression tools will be used to identify genes differentially expressed during iron stress conditions. Affymetrix GeneChips will be used to identify genes involved in yield, seed composition and other important traits in soybean. Bioinformatics will be used to position these genes on the whole genome sequence and the genetic map. Bioinformatic and experimental approaches will be used to identify and map genes differentially expressed during defense response and to identify and map defensin-like genes. A set of comparative molecular-evolutionary protocols will be used to make systematic and integrated use of large amounts of new genomic and functional data. Analyses will include comparison of homeologous regions, phylogenetic comparisons, and annotation of specific genomic regions.
Progress was made on all three objectives and their subobjectives, all of which fall under activities of crop informatics, genomics and genetic analyses, and mapping of important traits. Under Objective 1.A we made significant progress identifying and mapping genes that are differentially expressed during iron stress conditions. We used hybridization and next-generation sequencing to identify the genes. We then correlated candidates with genome sequence positions. We sequenced regions of several hundreds of candidate genes and identified markers associated with candidate genes and have begun assays of germplasm. We also made significant progress identifying genes involved in yield, seed composition and other important traits in soybean (Objective 1.B). We correlated candidates with chromosomal positions. Then, again, we identified markers associated with the candidate genes and carried out assays of high and low protein germplasm. Under Objective 2.A we made significant progress using bioinformatic and experimental approaches to identify and map genes differentially expressed during defense responses. We sequenced nine BACs (475,000 basepairs) located in the Rpp4 Asian Soybean Rust Resistance locus in the resistant parent PI459025B. Nine candidate Rpp4 resistance genes were identified. The expression of all nine genes was determined in different tissues at different times following infection and mock-infection with Asian Soybean Rust. Based on these analyses, we believe that a single gene, Rpp4R8, is responsible for resistance. This gene will be inserted into susceptible soybean varieties. We began using Virus Induced Gene Silencing constructs to target the Rpp2 Asian Soybean Rust resistance genes to identify other soybean diseases on linkage group J. These constructs are being used to identify genes controlling Phytopthora stem rot, Powdery Mildew and Brown Stem Rot resistance. Under Objective 2.B we made progress using bioinformatics and experimental approaches to identify and map defensin-like genes. The defensin-like gene models developed from Medicago truncatula and Arabidopsis thaliana have been used to screen the recent release of the soybean genome sequence. We are working to isolate proteins to survey antimicrobial activity of soybean defense-like genes corresponding to regions of the soybean genome associated with disease resistance. Under Objective 3 we made significant progress conducting comparative analyses of legume genes and genomes to place agronomically important genes in evolutionary and genome sequence context. We have begun to address the rarity of Asian Soybean Rust resistance in soybean and other legumes. Phylogenetic analyses suggest that one of the two ancestral species making up common day soybean may have been lacking Rpp4-like genes entirely. However, it is unclear if the genes were deleted from the ancestral parent, or may have never existed. To answer this question, we have developed markers for Rpp4-like genes that we are using to screen against 25 other legumes. We are sequencing three BACs from the Rpp4 region in Phaseolus vugaris to compare the evolution of this gene family in different legume species.
An encyclopedia of gene messages was developed for soybean. Soybean has nearly 50,000 genes within its genome. The function of most of these genes is unknown. Knowing when and how much each gene is turned on will help determine gene function. ARS scientists at Ames, Iowa analyzed many millions of gene messages from 14 tissues or stages of development. This resulted in a gene Atlas. This information will be critical to the eventual identification of function of all the genes in the genome.
Novel methods of mapping introgressed DNA in near-isogenic lines (NILs). The ARS has generated many soybean near-isogenic lines in which common genetic backgrounds differ by a single trait. These lines are invaluable for isolating genes causing the trait of interest. The problem is that identifying the region(s) of chromosomes that differ between lines is difficult. Novel technologies were been applied to show that introgressed DNA can be identified quickly and with more precision than commonly used genetic mapping techniques. This will make it easier and cheaper to pin-point possible locations of agronomically important genes and will speed crop improvement.
Gene expression is correlated with gene and genome structure. The literature is filled with conflicting reports about the correlation of gene expression patterns and gene/genomic structure. Most of these studies considered only how broadly or by how much a gene was expressed, not both. ARS scientists at Ames, Iowa considered both breadth and level of expression simultaneously and used expression data collected from 14 tissues. They found that the size of gene messages changes depending upon the level of expression and whether the expression is in a single tissue or many tissues. In fact, a 180 degree ‘flip’ in parameters was seen as a broad gamut of patterns was examined. This explained many of the contradictory results reported in the literature. These results are important for understanding genome evolution as it related to plant adaptation and for understanding the regulation of gene expression.
The Soybean genome sequence has been integrated with the genetic map. The genome of the soybean is large and complex making assembly of the DNA sequence difficult. ARS researchers at Ames, IA, working with the Department of Energy and ARS collaborators in Beltsville, MD helped to assemble the sequence and to integrate the whole genome sequence with the soybean genetic map. Thus, the sequence is now correlated with soybean traits collected over the last 30 years (~85 distinct mapped traits). It is now possible for plant breeders to begin to understand at a molecular level the hereditary code that contributes to the traits for which they breed. This is an important addition to our knowledge about soybean and, much as has been the case for research on human diseases, will be invaluable as soybean scientists work to develop improved varieties.
Du, J., Grant, D.M., Tian, Z., Nelson, R., Zhu, L., Shoemaker, R.C., Ma, J. 2010. SoyTEdb: A Comprehensive Database of Transposable Elements in the Soybean Genome. Biomed Central (BMC) Genomics. 11:113.
Hyten, D.L., Cannon, S.B., Song, Q., Weeks, N.T., Fickus, E.W., Shoemaker, R.C., Specht, J.E., May, G.D., Cregan, P.B. 2010. High-Throughput SNP Discovery through Deep Resequencing of a Reduced Representation Library to Anchor and Orient Scaffolds in the Soybean Whole Genome Sequence. Biomed Central (BMC) Genomics. 11:38.