Scientists created an advanced biological plant sensor to monitor plant growths. Strigolactones are one of the most important plant hormones that have diverse classes. Now scientists have genetically encoded a strigolactone sensor that will help to understand growths and other activities.
An international research team from King Abdullah University of Science and Technology (KAUST), Saudi Arabia and the University of Dusseldorf worked together to develop the sensor. In the journal of Science Advances, researchers described, until the present, it was barely known that how Strigolactones work as there were very limited very few amount of tools to investigate.
Lead researcher and associate professor of KAUST, Salim Al-Babili said in a statement,“Strigolactones are unstable and occur at very low concentrations, which makes these hormones difficult to study”. Strigolactones not only regulate development within plants but also make easier to communicate with other organisms.
However, it’s still unknown the functions of the different strigolactones, but the only thing is known that during the binding with D14 receptor protein, strigolactones are sensed in plant cells. According to Phys, the genetically encoded strigolactone sensor is engineered to be produced within living cells.
During the binding Strigolactones include another protein complex named, SMXL that is a yellow luminescent enzyme, luciferase, derived from fireflies. Scientists collected green luciferase from sea pansy for the sensor to enable quantification of the effect of strigolactones. The main reason for using green luciferase is, it can produce same quantities as SMXL.
It would be easier to measure the level of strigolactone activity with the ration between two different colors of luminescence. The 2’R form of the strigolactones was already measured by researcher while checking the functionality, sensitivity, and specificity of the sensor. Now, the modular structure also determines the 2’R form. Al-Babili explained that the sensor would be effective for crop architecture optimization and combat damaging parasitic weeds.
