Translating Nitrogen Use Efficiency from models to crops
This project seeks to help better indentify orthologous genes as well as indentify how much variation exists in NUE targets in wheat.
Optimizing biological nitrogen (N) use is pivotal to maximizing crop yields and ameliorating the adverse environmental impacts of excess agricultural N application. Most cereal crops only take up about 50% of the applied N (Robertson, 1997). The other 50% of applied nitrogen is lost either to soil microbes, leeched from the soil during rains or chemically lost to the environment. New opportunities exist to provide gains in efficiency via the translation of basic research into application in crop species. With the advent of the wheat genome the opportunity now exist to indentify novel targets for breeding of N efficient crops in the most import UK crop species. However to translate our understandings of genes involved comparison of more distant plant species to the original model species is increasing difficult as little experimental evidence exists to help identify orthologous genes other than sequence alone. In wheat the problem is further complicated as polyploidy triples the potential targets one could study for nitrogen use efficiency.
The proposal seeks to help better indentify orthologous genes as well as indentify how much variation exists in NUE targets in wheat. We are proposing to collect RNA Seq data on the eight parents of a widely distributed and publically available mapping population (MAGIC elite) to add experimental evidence to help build connections between current knowledge in model species and wheat. We will grow these eight lines under both sufficient N and low N conditions to add further evidence of the effect N has on expression and better predict orthologs.
Dr Matthew Milner,
Postdoctoral researcher, National Institute of Agricultural Botany (NIAB), Cambridge
Dr Mariana Fazenda,
Innovation and Enterprise Project Officer, Department of Plant Sciences, University of Cambridge
Prof Mario Caccamo,
Head of Crop Bioinformatics, NIAB, Cambridge
Dr Dan Swan,
Lead of Platforms and Pipelines group, Earlham Institute, Norwich