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United States Department of Agriculture

Agricultural Research Service

Title: Tilling to detect induced mutations in soybean

item Cooper, Jennifer
item Till, Bradley
item Laport, Robert
item Darlow, Margaret
item Kleffner, Justin
item Jamai, Aziz
item El-mellouki, Tarik
item Lui, Shiming
item Ritchie, Rae
item Nielsen, Niels
item Bilyeu, Kristin
item Meksem, Khalid
item Comai, Luca
item Henikof, Steven

Submitted to: Biomed Central (BMC) Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/24/2008
Publication Date: 1/24/2008
Citation: Cooper, J., Till, B., Laport, R., Darlow, M., Kleffner, J., Jamai, A., El-Mellouki, T., Lui, S., Ritchie, R.D., Nielsen, N.C., Bilyeu, K.D., Meksem, K., Comai, L., Henikof, S. 2008. Tilling to detect induced mutations in soybean. Biomed Central (BMC) Plant Biology. 8:9.

Interpretive Summary: Crop improvement has long relied on the selection of desirable traits after the use of mutagenesis. However, many desirable changes in soybean can only be achieved by identifying and combining multiple variant alleles. The purpose of this research was to characterize the ability to use mutagenesis to develop additional tools for soybean improvement, namely a reverse genetics procedure termed TILLING (targeting induced local lesions in genomes). Reverse genetics allows the selection of targeted variant alleles that are predicted to control desirable traits. The advantages of TILLING lines is that they immediately produce a perfect molecular marker that can be used in marker assisted breeding, they allow the combination of multiple alleles to produce the full phenotypic effect, and the lines can be directly used in breeding programs since they are not genetically modified organisms. TILLING populations can be used to select individual plants containing variant alleles of target genes. Independent mutant populations were developed and tested for their ability to contribute to a high throughput TILLING screening system. Mutant soybean populations were identifed that were found to have sufficient mutation density to warrant their use in TILLING. The impact of this research is that the soybean research community has several populations that will serve as a novel and valuable tool to identify variant alleles of genes controlling important traits.

Technical Abstract: Soybean (Glycine max L. Merr.) is an important nitrogen-fixing crop that provides much of the world’s protein and oil. However, the available tools for investigation of soybean gene function are limited. Nevertheless, chemical mutagenesis can be applied to soybean followed by screening for mutations in a target of interest using a strategy known as TILLING (Targeting Induced Local Lesions in Genomes). We have applied TILLING to four mutagenized soybean populations, three of which were treated with ethyl methanesulfonate (EMS) and one with N-nitroso-N-methylurea (NMU). We screened seven targets in each population and discovered a total of 116 induced mutations. The NMU-treated population and one EMS mutagenized population had similar mutation frequencies (~1/140kb), while another EMS population had a mutation frequency of ~1/250kb. The remaining population, which was treated with a lower concentration of EMS, showed a mutation frequency of ~1/550kb. Because of soybean’s polyploid history, PCR amplification of multiple targets could impede mutation discovery. Indeed, one set of primers tested in this study amplified more than a single target and produced low quality data. To address this problem, we removed an extraneous target by pretreating genomic DNA with a restriction enzyme. Digestion of the template eliminated amplification of the extraneous target and allowed the identification of four additional mutant alleles compared to untreated template. The development of four independent populations with considerable mutation density, together with an additional method for screening closely related targets, indicates that soybean is a suitable organism for high-throughput mutation discovery.

Last Modified: 10/19/2017
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