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United States Department of Agriculture

Agricultural Research Service

Research Project: FUNCTIONAL AND STRUCTURAL GENETIC ANALYSIS OF SOYBEAN AND OTHER LEGUMES

Location: Corn Insects and Crop Genetics Research

2009 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.


3.Progress Report
Through the course of the year, we have worked on six major projects (P1-P6). P1: We sequenced seven Bacterial Artificial Chromosome (BACs) (709,152 basepairs) located in the Rpp2 (resistance to Asian Soybean Rust (ASR)) locus in the susceptible parent Williams82. Multiple sequence alignments of twenty-three candidate resistance genes were used to develop Virus Induced Gene Silencing Constructs (VIGS). These constructs will be used to turn off the Rpp2 resistance gene and make genetically resistant plants susceptible to ASR. In addition, we are developing genomic tools from Rpp2 resistant parent to aid in the final identification of Rpp2. P2: In order to identify candidate genes for the Rpp4 ASR gene, we have sequenced two BACs from the Rpp4 locus in the susceptible parent Williams82. The sequence information was used to amplify candidate resistance gene sequences from the resistant parent. Using a combination of molecular tools including VIGS, we have identified a single candidate gene we believe corresponds to Rpp4. Currently, we are using a BAC library developed from the Rpp4 resistant parent to clone the full sequence of the gene. P3: We updated the annotation of the Soybean Affymetrix Genechip Genome Array (now version.
2)and developed bioinformatic software to allow statistical analysis of expression data based on the gene ontology. P4: We have chosen 10 soybean defensin-like genes for functional analyses. The 10 genes have been inserted into the P. pastoris expression system, allowing for production and purification of defensin proteins. These proteins are being tested for antimicrobial activity against a panel of legume pathogens. P5: Whole genome expression analyses were conducted to identify genes responding to iron. Analyses were made between iron efficient and inefficient genotypes in iron depleted and iron replete conditions. Differentially expressed genes were aligned to the whole genome sequence. Clustering of genes in the genome and the identification of common promoter motifs within clustered genes suggest the expression of genes within a cluster is coordinately regulated. P6: We have physically defined a protein Qualitative Trait Loci (QTL) spanning a ~8.4 million base pairs (Mbp) region on chromosome 20. This region was saturated with 36 new Simple Sequence Repeat (SSR) markers. Using backcross populations, 22 additional recombinant lines with recombination within the QTL interval were identified. Progeny from 17 of these recombinants were genotyped to confirm the recombination break points. We identified a duplicated region corresponding to the chromosome 20 protein QTL region. The duplicated region spans ~7.3 Mbp on chromosome 10. The comparative analysis of the regions indicates that these regions were likely to be products of the recent genome duplication in soybean ~14 MYA. Both regions appeared to be hotspots for transposon accumulation. The duplicated genes showed evidence of tissue specific expression.


4.Accomplishments
1. Identification of a single candidate resistance gene for Rpp4-mediated resistance to Asian Soybean Rust. ARS scientists at Ames, Iowa initiated an international collaboration including scientists from Embrapa Soybean (Brazil), Iowa State University and the USDA-ARS. Using a special USDA-ARS/Iowa State DNA library, a region of the genome corresponding to the Rpp4 Asian Soybean Rust resistance gene in the susceptible parent Williams82 was decoded and candidate resistance genes were identified. Analyses of the DNA code revealed that the Rpp4 region contained three candidate resistance genes. By combining genetic mapping, sequencing, semi-quantitative RT-PCR and Virus-induced Gene Silencing a single candidate gene for Rpp4 was identified. Outbreaks of Asian Soybean Rust have now occurred in all major soybean-producing countries and can cause yield losses up to 75% resulting in many billions of dollars lost. Thus far, only four genes (Rpp1, Rpp2, Rpp3 and Rpp4) conferring resistance to Asian Soybean Rust have been identified. Cloning of Rpp4 and the development of markers linked to the gene will aid in breeding efforts for resistance to ASR and other important soybean pathogens thus potentially saving growers billions of dollars per year.

2. Development of bioinformatic tools to analyze microarray and other expression data. Glass slides containing tens of thousand of soybean genes are used to measure whole-genome gene expression. Unfortunately, a convenient way to determine the probable identity of a gene represented on the array did not exist. ARS scientists at Ames, Iowa updated the annotation of the Soybean Affymetrix GeneChip Genome Array to Version 2. The gene messages Expressed Sequence Tag (ESTs) represented on the array were compared to the recently released soybean genome sequence. If ESTs mapped to a particular gene, the entire gene sequence was used to annotate the function of the gene. Comparisons to Arabidopsis genes and other publicly available gene sequences were also conducted. Bioinformatic software to allow statistical analysis of expression data based on the function of differentially expressed genes was developed as part of this tool. This accomplishment speeds the translation of raw expression data into useful biological information and speeds the migration of experimental results into crop improvement. Thus allowing genomic advancements to get to the field faster.


Review Publications
Meyer, J.D., Silva, D.C., Yang, C., Van De Mortel, M., Pedley, K.F., Hill, J.H., Shoemaker, R.C., Abdelnoor, R., Whitham, S.A., Graham, M.A. 2009. Identification and Analyses of Candidate Genes for Rpp4 Mediated Resistance to Asian Soybean Rust in Soybean (Glycine max). Plant Physiology. 150:295-307.

Mathieu, M., Winters, E., Kong, F., Wan, J., Want, S., Eckert, H., Donovan, C., Somers, D., Wang, K., Nguyen, H., Shoemaker, R.C., Stacey, G., Clemente, T. 2008. Establishment of a Soybean (Glycine max L. Merr.) Transposon-based Mutagenesis Repository. Planta. 229:279-289.

Narayanan, N.N., Tasma, I.M., Grant, D.M., Shoemaker, R.C., Bhattacharyya, M.K. 2009. Identification of Candidate Signaling Genes Including Regulators of Chromosome Condensation 1 Protein Family Differentially Expressed in the Soybean - Phytophthora Sojae Interaction. Journal of Theoretical and Applied Genetics. 118(3):399-412.

O'Rourke, J.A., Nelson, R., Grant, D.M., Schmutz, J., Grimwald, J., Cannon, S.B., Vance, C.P., Graham, M.A., Shoemaker, R.C. 2009. Integrating Microarray Analysis and the Soybean Genome to Understand the Soybean's Iron Deficiency Response. Biomed Central(BMC)Genomics. 10:376. Available: http://www.biomedcentral.com/1471-2164/10/376.

Last Modified: 7/22/2014
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