2013 Annual Report
1a.Objectives (from AD-416):
1)Develop molecular markers (SNPs) that distinguish iron efficient and iron inefficient soybean. 2)Screen ~ 350 genotypes representing public and private IDC breeding lines and cultivars. 3)Score ~ 30 previously untested Accessions and Plant Introductions for iron efficiency and screen them against the markers developed in #1 above. 4)Correlate molecular marker scores with iron efficiency scores. 5)Identify the markers optimal for selecting iron efficient germplasm and make the markers publicly available. 6)Analyze next-generation transcriptome data to identify metabolic pathways involved in iron stress response for the purpose of identifying new candidate genes.
1b.Approach (from AD-416):
Use the soybean whole genome sequence to search two major iron QTL regions to identify all recognizable regulatory genes and all 'candidate’ genes residing within the QTL region. Regulatory genes and selected candidate genes will be re-sequenced in eight genotypes representing iron efficient and iron inefficient soybean types (Efficient: Clark, A15, Hawkeye, and PI 437654; Inefficient: Anoka, BSR 101, Pride B216, T203). From this resequencing single nucleotide polymorphisms will be identified and SNP markers will be designed. The SNPs will be screened against approximately 350 genotypes representing midwest breeding lines. Screening will done using the 'Sequenome' technology at Iowa State University. Statistical methods will be employed to identify those markers with prediction capabilities in IDC breeding programs. Markers selected will be made available to the public Computationally analyze existing gene expression data to identify metabolic pathways involved in iron stress response. Map these pathways onto SoyCyc and make the information available on SoyBase.
During the analysis of the gene expression data, we identified a gene involved in DNA replication and repair, Replication Protein A (RPA), that was down regulated in our iron efficient line but not in the iron inefficient line. This suggested to us that RPA may be involved in the iron stress response. Previously, a gene coding for RPA3, the 14 kDa subunit, was identified as differentially expressed between iron efficient and inefficient lines Clark and Isoclark, respectively, in a microarray study. Quantitative reverse-transcription PCR (qRT-PCR) confirmed gene expression for all copies of all three RPA subunits (RPA1, RPA2 and RPA3) is down-regulated in Clark (iron efficient) after 24 hours of iron stress, while Isoclark (iron inefficient) has the opposite expression pattern. To test the hypothesis that down-regulation of the RPA gene is necessary for an iron efficient response, two homeologs of RPA3 were silenced simultaneously in Isoclark during iron stress. To do this, we used viral induced gene silencing (VIGS). Upon RPA3 silencing, iron deficiency chlorosis (IDC) visual score and chlorophyll content improved. The differential expression pattern between the two Nearly Isogenic Lines (NIL) as well as improved IDC symptoms upon RPA3 silencing in Isoclark suggest a link between DNA replication and the iron stress response. Numerous transcription factors and structural genes were identified as differentially expressed within six hours of iron stress. Six structural genes and eight transcription factors have been assayed at one, two, three, four, five and six hours post-stress using RT-PCR. Patterns of expression are being analyzed and will provide information on cascades of gene expression during iron stress in soybeans. This work reveals possible mechanisms by which soybean responds to iron deficiency, a nutritional disease causing millions of dollars in lost production each year.