Capturing drought-avoidance genotypes using peroxisome proliferation readout

CSANR Project 169

Status: Ongoing

Project Summary

Drought significantly affects agriculture in the US and has resulted in $4 billion in losses in just 2014 alone. Optimization of water management together with improved agricultural practices has caused major yield increases without additional water input. The next significant improvement in sustainable water usage is predicted to be in breeding crops with better performance under limited water availability. One of the key strategies of surviving drought is an avoidance mechanism, which depends on the ability of root system to reach moisture at deeper soil layers. Breeding crops with deeper and more extensive root system could potentially improve water use efficiency, however root phenotyping in large populations remains an expensive and time-consuming process. The goal of the proposed here research is to fill this technological gap by developing a simple and GMO-free technology for phenotyping root system morphology using spring wheat as a model system. Our approach is based of the preliminary findings that: (1) drought tolerance negatively correlates with abundance of small organelles peroxisomes in leaf cells; and (2) root biomass negatively correlates with peroxisome abundance in leaves. Hence, peroxisomes can be used as proxy of root system morphology under drought conditions. We will test our hypothesis by establishing correlation between size and overall architecture of root system, peroxisome parameters, and yield in a population of spring wheat genotypes. More efficient root phenotyping would facilitate breeding spring wheat varieties with more sustainable water usage in the irrigated fields and with higher productivity of dryland farming in the Pacific Northwest. Furthermore, our technology could potentially be applied for breeding other crops with more economical water usage.

Annual Entries

2017

Principal Investigator: Andrei Smertenko
Additional Investigator: Karen Sanguinet
Grant Amount: $26150