Metabolite changes in the tree can help us detect Huanglongbing
Article written by Carolyn Slupsky, Elizabeth Chin, Elizabeth Grafton-Cardwell, Peggy G. Lemaux, & Lukasz Stelinski.
Revised November 13, 2019
What is the technique?
Metabolism is all of the processes an organism carries out in order to stay alive. There are two types of molecules involved in an organism’s metabolism: proteins and small molecular weight chemicals called metabolites. These molecules change in abundance when the metabolism of a living organism, such as a human, insect, or tree, is altered. Measuring these molecules provides a ‘snapshot’ of an organism’s metabolism. During infection, an organism’s metabolism is busy responding to the pathogen, as it works to protect itself. Thus, the ‘metabolite profile’ is different for healthy versus infected organisms. Carolyn Slupsky is developing methods to measure metabolite profiles of citrus trees, and to identify one that indicates early CLas (the bacteria that causes HLB) infection.
How can metabolites be used to identify infected trees?
Who is working on this project?
Carolyn Slupsky, a Professor in the Department of Food Science and Technology at the University of California, Davis, and her research team are using these methods to establish and validate metabolite-based markers for early detection of HLB.
What are the challenges and opportunities?
The main challenge is field validation, because truly healthy, non-CLas-infected trees are not guaranteed in the field if the psyllid vector is present. However, this method has been successful in correctly identifying CLas-infected field trees in Texas, and more field trials using screen-protected healthy control trees are underway. Preliminary data shows that the metabolite profiles of leaves infected with citrus tristeza virus (CTV), canker, or citrus stubborn infections are different from the metabolite profile of leaves infected with CLas. Although sampling is as easy as clipping one leaf from a tree, analysis of the NMR spectrum is currently a bottleneck because this method is only semi-automated, and requires lab researcher input. Automation of NMR spectral analysis would help turn this into a high-throughput method.
Funding source: This project is funded by the Citrus Research Board and a USDA-NIFA Hatch grant.