Transposon tagging and fast neutron mutagenesis in Soybean
Necessary resources to aid in the translation of genomics information into applied technologies
Soybeans are the world’s single greatest source of both vegetable oil and protein. Soybean oil and protein is already a substrate for numerous industrial and alimentary uses. It is expected that new, designer oils and proteins can be used for everything including healthier diets, nutraceuticals, novel industrial compounds, and biodiesel. Genomic studies of soybean are targeted at understanding the biology behind its unique ability to produce high amounts of both oil and protein. However, the function of the predicted 45-50,000 genes in the soybean genome are primarily unknown. Thus, a key priority of the soybean breeding community has been the development of reverse genetic tools.
Active transposable elements have the ability to generate novel single copy insertions, thus, reducing the number of transformations needed to produce a mutant population. This is especially crucial for soybean, where the transformation efficiency is relatively low. We also are developing activation tagging resources that are designed to induce mutations that alter expression patterns in addition to disrupting coding regions. This project includes three transposon-based strategies: the tobacco retrotransposable element Tnt1, the maize Ac/Ds transposon, and the rice miniature inverted terminal repeat element (MITE) transposon mPing.
In addition we are using fast-neutron-induced deletions to produce mutant populations. The mutations in this population will be characterized using high throughput screening methods.
As mutant lines are produced, information about the mutagen, transposon insertion sites, and associated phenotypes is deposited into a public database. Our long-term goal is to make these mutant lines available through a seed bank facility.
Contacts
Wayne Parrott, University of Georgia
Tom Clemente, University of Nebraska
Gary Stacey, University of Missouri, Columbia
Carroll Vance, University of Minnesota
Zhanyuan J. Zhang, University of Missouri, Columbia
This project supported by NSF grant #0820769